数学建模优秀论文【精品资料】免费下载

承诺书我们仔细阅读了中国大学生数学建模竞赛的竞赛规则.我们完全明白，在竞赛开始后参赛队员不能以任何方式（包括电话、电子邮件、网上咨询等）与队外的任何人（包括指导教师）研究、讨论与赛题有关的问题。

我们知道，抄袭别人的成果是违反竞赛规则的, 如果引用别人的成果或其他公开的资料（包括网上查到的资料），必须按照规定的参考文献的表述方式在正文引用处和参考文献中明确列出。

我们郑重承诺，严格遵守竞赛规则，以保证竞赛的公正、公平性。

如有违反竞赛规则的行为，我们将受到严肃处理。

我们参赛选择的题号是（从A/B/C/D中选择一项填写）： C我们的参赛报名号为（如果赛区设置报名号的话）：所属学校（请填写完整的全名）：参赛队员(打印并签名) ：1.2.3.指导教师或指导教师组负责人(打印并签名)：日期： 2010 年 9 月 10 日赛区评阅编号（由赛区组委会评阅前进行编号）：编号专用页赛区评阅编号（由赛区组委会评阅前进行编号）：赛区评阅记录（可供赛区评阅时使用）：全国统一编号（由赛区组委会送交全国前编号）：全国评阅编号（由全国组委会评阅前进行编号）：输油管的布置摘要“输油管的布置”数学建模的目的是设计最优化的路线，建立一条费用最省的输油管线路，但是不同于普遍的最短路径问题，该题需要考虑多种情况，例如，城区和郊区费用的不同，采用共用管线和非公用管线价格的不同等等。

我们基于最短路径模型，对于题目实际情况进行研究和分析，对三个问题都设计了合适的数学模型做出了相应的解答和处理。

问题一：此问只需考虑两个加油站和铁路之间位置的关系，根据位置的不同设计相应的模型，我们基于光的传播原理，设计了一种改进的最短路径模型，在不考虑共用管线价格差异的情况下，只考虑如何设计最短的路线，因此只需一个未知变量便可以列出最短路径函数；在考虑到共用管线价格差异的情况下，则需要建立2个未知变量，如果带入已知常量，可以解出变量的值。

问题二：此问给出了两个加油站的具体位置，并且增加了城区和郊区的特殊情况，我们进一步改进数学模型，将输油管路线横跨两个不同的区域考虑为光在两种不同介质中传播的情况，输油管在城区和郊区的铺设将不会是直线方式，我们将其考虑为光在不同介质中传播发生了折射。

优秀的数学建模论文范文第1篇摘要：将数学建模思想融入高等数学的教学中来，是目前大学数学教育的重要教学方式。

建模思想的有效应用，不仅显著提高了学生应用数学模式解决实际问题的能力，还在培养大学生发散思维能力和综合素质方面起到重要作用。

本文试从当前高等数学教学现状着手，分析在高等数学中融入建模思想的重要性，并从教学实践中给出相应的教学方法，以期能给同行教师们一些帮助。

关键词：数学建模；高等数学；教学研究一、引言建模思想使高等数学教育的基础与本质。

从目前情况来看，将数学建模思想融入高等教学中的趋势越来越明显。

但是在实际的教学过程中，大部分高校的数学教育仍处在传统的理论知识简单传授阶段。

其教学成果与社会实践还是有脱节的现象存在，难以让学生学以致用，感受到应用数学在现实生活中的魅力，这种教学方式需要亟待改善。

二、高等数学教学现状高等数学是现在大学数学教育中的基础课程，也是一门必修的课程。

他能为其他理工科专业的学生提供很多种解题方式与解题思路，是很多专业，如自动化工程、机械工程、计算机、电气化等必不可少的基础课程。

同时，现实生活中也有很多方面都涉及高数的运算，如，银行理财基金的使用问题、彩票的概率计算问题等，从这些方面都可以看出人们不能仅仅把高数看成是一门学科而已，它还与日常生活各个方面有重要的联系。

但现在很多学校仍以应试教育为主，采取填鸭式教学方式，加上高数的教材并没有与时俱进，将其与生活的关系融入教材内，使学生无法意识到高数的重要性以及高数在日常生活中的魅力，因此产生排斥甚至对抗的心理，只是在临考前突击而已。

因此，对高数进行教学改革是十分有必要的，而且怎么改，怎么让学生发现高数的魅力，并积极主动学习高数也是作为教师所面临的一个重大问题。

三、将数学建模思想融入高等数学的重要性第一，能够激发学生学习高数的兴趣。

建模思想实际上是使用数学语言来对生活中的实际现象进行描述的过程。

把建模思想应用到高等数学的学习中，能够让学生们在日常生活中理解数学的实际应用状况与解决日常生活问题的方便性，让学生们了解到高数并不只是一门课程，而是整个日常生活的基础。

初中数学建模优秀论文试论数学建模方法目前数学教学与数学应用脱节的现象很突出，以至于学生认为学习数学没用，对数学学习失去兴趣，如何改变目前这种教学与应用脱节的现象，笔者认为，可以用数学模型法指导数学应用题教学，为学生用数学来解决问题提供经验和范式，从而探索出一条行之有效的教学途径。

一、什么是数学模型要突出应用，就应站在数学模型法的高度来认识并实施应用题教学。

什么是数学模型法?数学模型法就是把实际问题加以抽象概括，建立相应的数学模型，利用这些模型来研究实际问题的一般数学方法。

教师在应用题教学中要渗透这种方法和思想，要注重并强调如何从实际问题中发现并抽象出数学问题，如何用数学模型(包括数学概念、公式、方程、不等式函数等)来表达实际问题，如何用数学模型的解来解释实际问题的解。

以及为科学决策提供可信的依据并预测其发展趋势。

二、建模示范方法例谈在教学中我根据教学内容，选编一些应用问题进行例题教学，引导学生分析联想、抽象建模，培养学生的建模能力，提供经验和范式。

选编数学应用性例题的一般原则是：①必须与教学内容密切联系;②必须与学生的知识水平相适应;③必须符合科学性和趣味性;④取材应尽量涉及目前社会的热点问题，有时代气息，有教育价值。

1.与其他相关学科有关的问题题1：化学中甲烷CH4的键角109°28′是怎样求出来的?题2：在大楼底层有一控制室，有三条导线和楼上某电器相连，设三连导线的电阻分别为x、y、z，现手头有一只电表可在控制室内测量电阻，试没计一种数学方法求这三根导线的电阻。

2.发生在学生身边的数学问题题3：学校教学大楼，从一楼到二楼共13个台阶。

一位同学上楼梯可以一步上一个台阶，也可以一步上两个台阶。

问从一楼走到二楼，有多少种不同走法?一年365天，每天选用一种走法，能否做到天天的走法均不相同?题4：学校足球场地是一个102×68平方米的矩形，球门宽为8米，由边线下底传中是惯用的战术，请你帮助足球队员确定离底线多少距离的地方起脚传中效果最佳?3.从教材的例题和习题中改造而成的问题课本中有一习题，稍加修改就可以形成以下应用问题。

题目：How Much Gas Should I Buy This Week?题目来源：2012年第十五届美国高中生数学建模竞赛（HiMCM）B题获奖等级：特等奖，并授予INFORMS奖论文作者：深圳中学2014届毕业生李依琛、王喆沛、林桂兴、李卓尔指导老师：深圳中学张文涛AbstractGasoline is the bleed that surges incessantly within the muscular ground of city; gasoline is the feast that lures the appetite of drivers. “To fill or not fill?” That is the question flustering thousands of car owners. This paper will guide you to predict the gasoline prices of the coming week with the currently available data with respect to swift changes of oil prices. Do you hold any interest in what pattern of filling up the gas tank can lead to a lower cost in total?By applying the Time series analysis method, this paper infers the price in the imminent week. Furthermore, we innovatively utilize the average prices of the continuous two weeks to predict the next two week’s average price; similarly, employ the four-week-long average prices to forecast the average price of four weeks later. By adopting the data obtained from 2011and the comparison in different aspects, we can obtain the gas price prediction model ：G t+1=0.0398+1.6002g t+−0.7842g t−1+0.1207g t−2+ 0.4147g t−0.5107g t−1+0.1703g t−2+ε .This predicted result of 2012 according to this model is fairly ideal. Based on the prediction model,We also establish the model for how to fill gasoline. With these models, we had calculated the lowest cost of filling up in 2012 when traveling 100 miles a week is 637.24 dollars with the help of MATLAB, while the lowest cost when traveling 200 miles a week is 1283.5 dollars. These two values are very close to the ideal value of cost on the basis of the historical figure, which are 635.24 dollars and 1253.5 dollars respectively. Also, we have come up with the scheme of gas fulfillment respectively. By analyzing the schemes of gas filling, we can discover that when you predict the future gasoline price going up, the best strategy is to fill the tank as soon as possible, in order to lower the gas fare. On the contrary, when the predicted price tends to decrease, it is wiser and more economic for people to postpone the filling, which encourages people to purchase a half tank of gasoline only if the tank is almost empty.For other different pattern for every week’s “mileage driven”, we calculate the changing point of strategies-changed is 133.33 miles.Eventually, we will apply the models -to the analysis of the New York City. The result of prediction is good enough to match the actual data approximately. However, the total gas cost of New York is a little higher than that of the average cost nationally, which might be related to the higher consumer price index in the city. Due to the limit of time, we are not able to investigate further the particular factors.Keywords: gasoline price Time series analysis forecast lowest cost MATLABAbstract ---------------------------------------------------------------------------------------1 Restatement --------------------------------------------------------------------------------------21. Assumption----------------------------------------------------------------------------------42. Definitions of Variables and Models-----------------------------------------------------4 2.1 Models for the prediction of gasoline price in the subsequent week------------4 2.2 The Model of oil price next two weeks and four weeks--------------------------5 2.3 Model for refuel decision-------------------------------------------------------------52.3.1 Decision Model for consumer who drives 100 miles per week-------------62.3.2 Decision Model for consumer who drives 200 miles per week-------------73. Train and Test Model by 2011 dataset---------------------------------------------------8 3.1 Determine the all the parameters in Equation ② from the 2011 dataset-------8 3.2 Test the Forecast Model of gasoline price by the dataset of gasoline price in2012-------------------------------------------------------------------------------------10 3.3 Calculating ε --------------------------------------------------------------------------12 3.4 Test Decision Models of buying gasoline by dataset of 2012-------------------143.4.1 100 miles per week---------------------------------------------------------------143.4.2 200 miles per week---------------------------------------------------------------143.4.3 Second Test for the Decision of buying gasoline-----------------------------154. The upper bound will change the Decision of buying gasoline---------------------155. An analysis of New York City-----------------------------------------------------------16 5.1 The main factor that will affect the gasoline price in New York City----------16 5.2 Test Models with New York data----------------------------------------------------185.3 The analysis of result------------------------------------------------------------------196. Summery& Advantage and disadvantage-----------------------------------------------197. Report----------------------------------------------------------------------------------------208. Appendix------------------------------------------------------------------------------------21 Appendix 1(main MATLAB programs) ------------------------------------------------21 Appendix 2(outcome and graph) --------------------------------------------------------34The world market is fluctuating swiftly now. As the most important limited energy, oil is much accounted of cars owners and dealer. We are required to make a gas-buying plan which relates to the price of gasoline, the volume of tank, the distance that consumer drives per week, the data from EIA and the influence of other events in order to help drivers to save money.We should use the data of 2011 to build up two models that discuss two situations: 100miles/week or 200miles/week and use the data of 2012 to test the models to prove the model is applicable. In the model, consumer only has three choices to purchase gas each week, including no gas, half a tank and full tank. At the end, we should not only build two models but also write a simple but educational report that can attract consumer to follow this model.1.Assumptiona)Assume the consumer always buy gasoline according to the rule of minimumcost.b)Ignore the difference of the gasoline weight.c)Ignore the oil wear on the way to gas stations.d)Assume the tank is empty at the beginning of the following models.e)Apply the past data of crude oil price to predict the future price ofgasoline.(The crude oil price can affect the gasoline price and we ignore thehysteresis effect on prices of crude oil towards prices of gasoline.)2.Definitions of Variables and Modelst stands for the sequence number of week in any time.(t stands for the current week. (t-1) stands for the last week. (t+1) stands for the next week.c t: Price of crude oil of the current week.g t: Price of gasoline of the t th week.P t: The volume of oil of the t th week.G t+1: Predicted price of gasoline of the (t+1)th week.α,β: The coefficient of the g t and c t in the model.d: The variable of decision of buying gasoline.(d=1/2 stands for buying a half tank gasoline)2.1 Model for the prediction of gasoline price in the subsequent weekWhether to buy half a tank oil or full tank oil depends on the short-term forecast about the gasoline prices. Time series analysis is a frequently-used method to expect the gasoline price trend. It can be expressed as:G t+1=α1g t+α2g t−1+α3g t−2+α4g t−3+…αn+1g t−n+ε ----Equation ①ε is a parameter that reflects the influence towards the trend of gasoline price in relation to several aspects such as weather data, economic data, world events and so on.Due to the prices of crude oil can influence the future prices of gasoline; we will adopt the past prices of crude oil into the model for gasoline price forecast.G t+1=(α1g t+α2g t−1+α3g t−2+α4g t−3+⋯αn+1g t−n)+(β1g t+β2g t−1+β3g t−2+β4g t−3+⋯βn+1g t−n)+ε----Equation ②We will use the 2011 data set to calculate the all coefficients and the best delay periods n.2.2 The Model of oil price next two weeks and four weeksWe mainly depend on the prediction of change of gasoline price in order to make decision that the consumer should buy half a tank or full tank gas. When consumer drives 100miles/week, he can drive whether 400miles most if he buys full tank gas or 200miles most if he buys half a tank gas. When consumer drives 200miles/week, full tank gas can be used two weeks most or half a tank can be used one week most. Thus, we should consider the gasoline price trend in four weeks in future.Equation ②can also be rewritten asG t+1=(α1g t+β1g t)+(α2g t−1+β2g t−1)+(α3g t−2+β3g t−2)+⋯+(αn+1g t−n+βn+1g t−n)+ε ----Equation ③If we define y t=α1g t+β1g t,y t−1=α2g t−1+β2g t−1, y t−2=α3g t−2+β3g t−2……, and so on.Equation ③can change toG t+1=y t+y t−1+y t−2+⋯+y t−n+ε ----Equation ④We use y(t−1,t)denote the average price from week (t-1) to week (t), which is.y(t−1,t)=y t−1+y t2Accordingly, the average price from week (t-3) to week (t) isy(t−3,t)=y t−3+y t−2+y t−1+y t.4Apply Time series analysis, we can get the average price from week (t+1) to week (t+2) by Equation ④,G(t+1,t+2)=y(t−1,t)+y(t−3,t−2)+y(t−5,t−4), ----Equation ⑤As well, the average price from week (t+1) to week (t+4) isG(t+1,t+4)=y(t−3,t)+y(t−7,t−4)+y(t−11,t−8). ----Equation ⑥2.3 Model for refuel decisionBy comparing the present gasoline price with the future price, we can decide whether to fill half or full tank.The process for decision can be shown through the following flow chart.Chart 1For the consumer, the best decision is to get gasoline with the lowest prices. Because a tank of gasoline can run 2 or 4 week, so we should choose a time point that the price is lowest by comparison of the gas prices at present, 2 weeks and 4 weeks later separately. The refuel decision also depends on how many free spaces in the tank because we can only choose half or full tank each time. If the free spaces are less than 1/2, we can refuel nothing even if we think the price is the lowest at that time.2.3.1 Decision Model for consumer who drives 100 miles per week.We assume the oil tank is empty at the beginning time(t=0). There are four cases for a consumer to choose a best refuel time when the tank is empty.i.g t>G t+4and g t>G t+2, which means the present gasoline price is higherthan that either two weeks or four weeks later. It is economic to fill halftank under such condition. ii. g t <Gt +4 and g t <G t +2, which means the present gasoline price is lower than that either two weeks or four weeks later. It is economic to fill fulltank under such condition. iii. Gt +4>g t >G t +2, which means the present gasoline price is higher than that two weeks later but lower than that four weeks later. It is economic to fillhalf tank under such condition. iv. Gt +4<g t <G t +2, which means the present gasoline price is higher than that four weeks later but lower than that two weeks later. It is economic to fillfull tank under such condition.If other time, we should consider both the gasoline price and the oil volume in the tank to pick up a best refuel time. In summary, the decision model for running 100 miles a week ist 2t 4t 2t 4t 2t 4t 2t 4t 11111411111ˆˆ(1)1((1)&max(,))24442011111ˆˆˆˆ1/2((1)&G G G (&))(0(1G G )&)4424411ˆˆˆ(1)0&(G 4G G (G &)t i t i t t t t i t i t t t t t t i t t d t or d t g d d t g or d t g d t g or ++++----+++-++<--<<--<>⎧⎪=<--<<<--<<<⎨⎪⎩--=><∑∑∑∑∑t 2G ˆ)t g +<----Equation ⑦d i is the decision variable, d i =1 means we fill full tank, d i =1/2 means we fill half tank. 11(1)4t i tdt ---∑represents the residual gasoline volume in the tank. The method of prices comparison was analyzed in the beginning part of 2.3.1.2.3.2 Decision Model for consumer who drives 200 miles per week.Because even full tank can run only two weeks, the consumer must refuel during every two weeks. There are two cases to decide whether to buy half or full tank when the tank is empty. This situation is much simpler than that of 100 miles a week. The process for decision can also be shown through the following flow chart.Chart 2The two cases for deciding buy half or full tank are: i. g t >Gt +1, which means the present gasoline price is higher than the next week. We will buy half tank because we can buy the cheaper gasoline inthe next week. ii. g t <Gt +1, which means the present gasoline price is lower than the next week. To buy full tank is economic under such situation.But we should consider both gasoline prices and free tank volume to decide our refueling plan. The Model is111t 11t 111(1)1220111ˆ1/20(1)((1)0&)22411ˆ(1&G )0G 2t i t t i t i t t t t t i t t d t d d t or d t g d t g ----++<--<⎧⎪=<--<--=>⎨⎪⎩--=<∑∑∑∑ ----Equation ⑧3. Train and Test Model by the 2011 datasetChart 33.1 Determine all the parameters in Equation ② from the 2011 dataset.Using the weekly gas data from the website and the weekly crude price data from , we can determine the best delay periods n and calculate all the parameters in Equation ②. For there are two crude oil price dataset (Weekly Cushing OK WTI Spot Price FOB and Weekly Europe Brent SpotPrice FOB), we use the average value as the crude oil price without loss of generality. We tried n =3, 4 and 5 respectively with 2011 dataset and received comparison graph of predicted value and actual value, including corresponding coefficient.（A ） n =3(the hysteretic period is 3)Graph 1 The fitted price and real price of gasoline in 2011(n=3)We find that the nearby effect coefficient of the price of crude oil and gasoline. This result is same as our anticipation.（B）n=4(the hysteretic period is 4)Graph 2 The fitted price and real price of gasoline in 2011（n=4）(C) n=5(the hysteretic period is 5)Graph 3 The fitted price and real price of gasoline in 2011（n=5）Via comparing the three figures above, we can easily found that the predictive validity of n=3(the hysteretic period is 3) is slightly better than that of n=4(the hysteretic period is 4) and n=5(the hysteretic period is 5) so we choose the model of n=3 to be the prediction model of gasoline price.G t+1=0.0398+1.6002g t+−0.7842g t−1+0.1207g t−2+ 0.4147g t−0.5107g t−1+0.1703g t−2+ε----Equation ⑨3.2 Test the Forecast Model of gasoline price by the dataset of gasoline price in 2012Next, we apply models in terms of different hysteretic periods(n=3,4,5 respectively), which are shown in Equation ②,to forecast the gasoline price which can be acquired currently in 2012 and get the graph of the forecast price and real price of gasoline:Graph 4 The real price and forecast price in 2012（n=3）Graph 5 The real price and forecast price in 2012（n=4）Graph 6 The real price and forecast price in 2012（n=5）Conserving the error of observation, predictive validity is best when n is 3, but the differences are not obvious when n=4 and n=5. However, a serious problem should be drawn to concerns: consumers determines how to fill the tank by using the trend of oil price. If the trend prediction is wrong (like predicting oil price will rise when it actually falls), consumers will lose. We use MATLAB software to calculate the amount of error time when we use the model of Equation ⑨to predict the price of gasoline in 2012. The graph below shows the result.It’s not difficult to find the prediction effect is the best when n is 3. Therefore, we determined to use Equation ⑨as the prediction model of oil price in 2012.G t+1=0.0398+1.6002g t+−0.7842g t−1+0.1207g t−2+ 0.4147g t−0.5107g t−1+0.1703g t−2+ε3.3 Calculating εSince political occurences, economic events and climatic changes can affect gasoline price, it is undeniable that a ε exists between predicted prices and real prices. We can use Equation ②to predict gasoline prices in 2011 and then compare them with real data. Through the difference between predicted data and real data, we can estimate the value of ε .The estimating process can be shown through the following flow chartChart 4We divide the international events into three types: extra serious event, major event and ordinary event according to the criteria of influence on gas prices. Then we evaluate the value: extra serious event is 3a, major event is 2a, and ordinary event is a. With inference to the comparison of the forecast price and real price in 2011, we find that large deviation of data exists at three time points: May 16,2011, Aug 08,2011 andOct 10,2011. After searching, we find that some important international events happened nearly at the three time points. We believe that these events which occurred by chance affect the international prices of gasoline so the predicted prices deviate from the actual prices. The table of events and the calculation of the value of a areTherefore, by generalizing several sets of particular data and events, we can estimate the value of a:a=26.84 ----Equation ⑩The calculating process is shown as the following graph.Since now we have obtained the approximate value of a, we can evaluate the future prices according to currently known gasoline prices and crude oil prices. To improve our model, we can look for factors resulting in some major turning point in the graph of gasoline prices. On the ground that the most influential factors on prices in 2012 are respectively graded, the difference between fact and prediction can be calculated.3.4 Test Decision Models of buying gasoline by the dataset of 2012First, we use Equation ⑨to calculate the gasoline price of next week and use Equation ⑤and Equation ⑥to calculate the gasoline price trend of next two to four weeks. On the basis above, we calculate the total cost, and thus receive schemes of buying gasoline of 100miles per week according to Equation ⑦and Equation ⑧. Using the same method, we can easily obtain the pattern when driving 200 miles per week. The result is presented below.We collect the important events which will affect the gasoline price in 2012 as well. Therefore, we calculate and adjust the predicted price of gasoline by Equation ⑩. We calculate the scheme of buying gasoline again. The result is below:3.4.1 100 miles per weekT2012 = 637.2400 （If the consumer drives 100 miles per week, the total cost inTable 53.4.2 200 miles per weekT2012 = 1283.5 （If the consumer drives 200 miles per week, the total cost in 2012 is 1283.5 USD）. The scheme calculated by software is below：Table 6According to the result of calculating the buying-gasoline scheme from the model, we can know: when the gasoline price goes up, we should fill up the tank first and fill up again immediately after using half of gasoline. It is economical to always keep the tank full and also to fill the tank in advance in order to spend least on gasoline fee. However, when gasoline price goes down, we have to use up gasoline first and then fill up the tank. In another words, we need to delay the time of filling the tank in order to pay for the lowest price. In retrospect to our model, it is very easy to discover that the situation is consistent with life experience. However, there is a difference. The result is based on the calculation from the model, while experience is just a kind of intuition.3.4.3 Second Test for the Decision of buying gasolineSince the data in 2012 is historical data now, we use artificial calculation to get the optimal value of buying gasoline. The minimum fee of driving 100 miles per week is 635.7440 USD. The result of calculating the model is 637.44 USD. The minimum fee of driving 200 miles per week is 1253.5 USD. The result of calculating the model is 1283.5 USD. The values we calculate is close to the result of the model we build. It means our model prediction effect is good. (we mention the decision people made every week and the gas price in the future is unknown. We can only predict. It’s normal to have deviation. The buying-gasoline fee which is based on predicted calculation must be higher than the minimum buying-gasoline fee which is calculated when all the gas price data are known.)We use MATLAB again to calculate the total buying-gasoline fee when n=4 and n=5. When n=4,the total fee of driving 100 miles per week is 639.4560 USD and the total fee of driving 200 miles per week is 1285 USD. When n=5, the total fee of driving 100 miles per week is 639.5840 USD and the total fee of driving 200 miles per week is 1285.9 USD. The total fee are all higher the fee when n=3. It means it is best for us to take the average prediction model of 3 phases.4. The upper bound will change the Decision of buying gasoline.Assume the consumer has a mileage driven of x1miles per week. Then, we can use 200to indicate the period of consumption, for half of a tank can supply 200-mile x1driving. Here are two situations:<1.5①200x1>1.5②200x1In situation①, the consumer is more likely to apply the decision of 200-mile consumer’s; otherwise, it is wiser to adopt the decision of 100-mile consumer’s. Therefore, x1is a critical value that changes the decision if200=1.5x1x1=133.3.Thus, the mileage driven of 133.3 miles per week changes the buying decision.Then, we consider the full-tank buyers likewise. The 100-mile consumer buys half a tank once in four weeks; the 200-mile consumer buys half a tank once in two weeks. The midpoint of buying period is 3 weeks.Assume the consumer has a mileage driven of x2miles per week. Then, we can to illustrate the buying period, since a full tank contains 400 gallons. There use 400x2are still two situations:<3③400x2>3④400x2In situation③, the consumer needs the decision of 200-mile consumer’s to prevent the gasoline from running out; in the latter situation, it is wiser to tend to the decision of 100-mile consumer’s. Therefore, x2is a critical value that changes the decision if400=3x2x2=133.3We can find that x2=x1=133.3.To wrap up, there exists an upper bound on “mileage driven”, that 133.3 miles per week is the value to switch the decision for buying weekly gasoline. The following picture simplifies the process.Chart 45. An analysis of New Y ork City5.1 The main factors that will affect the gasoline price in New York CityBased on the models above, we decide to estimate the price of gasoline according to the data collected and real circumstances in several cities. Specifically, we choose New York City as a representative one.New York City stands in the North East in the United States, with the largest population throughout the country as 8.2 million. The total area of New York City is around 1300 km2, with the land area as 785.6 km2(303.3 mi2). One of the largest trading centers in the world, New York City has a high level of resident’s consumption. As a result, the level of the price of gasoline in New York City is higher than the average regular oil price of the United States. The price level of gasoline and its fluctuation are the main factors of buying decision.Another reasonable factor we expect is the distribution of gas stations. According to the latest report, there are approximately 1670 gas stations in the city area (However, after the impact of hurricane Sandy, about 90 gas stations have been temporarily out of use because of the devastation of Sandy, and there is still around 1580 stations remaining). From the information above, we can calculate the density of gas stations thatD(gasoline station)= t e amount of gas stationstotal land area =1670 stations303.3 mi2=5.506 stations per mi2This is a respectively high value compared with several other cities the United States. It also indicates that the average distance between gas stations is relatively small. The fact that we can neglect the distance for the cars to get to the station highlights the role of the fluctuation of the price of gasoline in New York City.Also, there are approximately 1.8 million residents of New York City hold the driving license. Because the exact amount of cars in New York City is hard to determine, we choose to analyze the distribution of possible consumers. Thus, we can directly estimate the density of consumers in New York City in a similar way as that of gas stations:D(gasoline consumers)= t e amount of consumerstotal land area = 1.8 million consumers303.3 mi2=5817consumers per mi2Chart 5In addition, we expect that the fluctuation of the price of crude oil plays a critical role of the buying decision. The media in New York City is well developed, so it is convenient for citizens to look for the data of the instant price of crude oil, then to estimate the price of gasoline for the coming week if the result of our model conforms to the assumption. We will include all of these considerations in our modification of the model, which we will discuss in the next few steps.For the analysis of New York City, we apply two different models to estimate the price and help consumers make the decision.5.2 Test Models with New York dataAmong the cities in US, we pick up New York as an typical example. The gas price data is downloaded from the website () and is used in the model described in Section 2 and 3.The gas price curves between the observed data and prediction data are compared in next Figure.Figure 6The gas price between the observed data and predicted data of New York is very similar to Figure 3 in US case.Since there is little difference between the National case and New York case, the purchase strategy is same. Following the same procedure, we can compare the gas cost between the historical result and predicted result.For the case of 100 miles per week, the total cost of observed data from Feb to Oct of 2012 in New York is 636.26USD, while the total cost of predicted data in the same period is 638.78USD, which is very close. It proves that our prediction model is good. For the case of 200 miles per week, the total cost of observed data from Feb to Oct of 2012 in New York is 1271.2USD, while the total cost of predicted data in the same period is 1277.6USD, which is very close. It proves that our prediction model is good also.5.3 The analysis of resultBy comparing, though density of gas stations and density of consumers of New York is a little higher than other places but it can’t lower the total buying-gas fee. Inanother words, density of gas stations and density of consumers are not the actual factors of affecting buying-gas fee.On the other hand, we find the gas fee in New York is a bit higher than the average fee in US. We can only analyze preliminary it is because of the higher goods price in New York. We need to add price factor into prediction model. We can’t improve deeper because of the limited time. The average CPI table of New York City and USA is below:Datas Statistics website(/xg_shells/ro2xg01.htm)6. Summery& Advantage and disadvantageTo reach the solution, we make graphs of crude oil and gasoline respectively and find the similarity between them. Since the conditions are limited that consumers can only drive 100miles per week or 200miles per week, we separate the problem into two parts according to the limitation. we use Time series analysis Method to predict the gasoline price of a future period by the data of several periods in the past. Then we take the influence of international events, economic events and weather changes and so on into consideration by adding a parameter. We give each factor a weight consequently and find the rules of the solution of 100miles per week and 200miles per week. Then we discuss the upper bound and clarify the definition of upper bound to solve the problem.According to comparison from many different aspects, we confirm that the model expressed byEquation ⑨is the best. On the basis of historical data and the decision model of buying gasoline(Equation ⑦and Equation ⑧), we calculate that the actual least cost of buying gasoline is 635.7440 USD if the consumer drives 100 miles per week (the result of our model is 637.24 USD) and the actual least cost of buying gasoline is 1253.5 USD(the result of our model is 1283.5 USD) if the consumer drives 100 miles per week. The result we predicted is similar to the actual result so the predictive validity of our model is finer.Disadvantages:1.The events which we predicted are difficult to quantize accurately. The turningpoint is difficult for us to predict accurately as well.2.We only choose two kinds of train of thought to develop models so we cannotevaluate other methods that we did not discuss in this paper. Other models which are built up by other train of thought are possible to be the optimal solution.。

Can We Assess a Health Care System's Performance?参赛队员：董希望（自动化学院），刘琳燕（城环学院）刘福亮（软件学院）指导教师：肖 剑参赛单位：重庆大学参赛时间：2008年2月15∼18日Can We Assess a Health Care System's Performance?1.BackgroundHealth systems consist of all the people and actions whose primary purpose is to improve health. They may be integrated and centrally directed, but often they are not. After centuries as small-scale, largely private or charitable, mostly ineffectual entities, they have grown explosively in this century as knowledge has been gained and applied. They have contributed enormously to better health, but their contribution could be greater still, especially for the poor. Failure to achieve that potential is due more to systemic failings than to technical limitations. It is therefore urgent to assess current performance and to judge how health systems can reach their potential.The World Health Organization (WHO) is a specialized agency of the United Nations (UN) that acts as a coordinating authority on international public health. Established on 7 April 1948, and headquartered in Geneva, Switzerland, the agency inherited the mandate and resources of its predecessor, the Health Organization, which had been an agency of the League of Nations.The WHO's constitution states that its objective "is the attainment by all peoples of the highest possible level of health." Its major task is to combat disease, especially key infectious diseases, and to promote the general health of the people of the world.As well as coordinating international efforts to monitor outbreaks of infectious diseases, such as SARS, malaria, and AIDS, the WHO also sponsors programs to prevent and treat such diseases. The WHO supports the development and distribution of safe and effective vaccines, pharmaceutical diagnostics, and drugs. The WHO also carries out various health-related campaigns — for example, to boost the consumption of fruits and vegetables worldwide and to discourage tobacco use.The annual World Health Report (http://www.who.int/whr/en/index.html) assesses global health factors and World Health Statistics provides health statistics for the countries in the UN. The production and dissemination of health statistics is a major function of the WHO. To many people, these data and the associated analyses are considered unbiased and very valuable to the world community.2. Basic Assumption and Hypotheses1.Assume that in a certain interval such as 5years the main components (metrics) ofthe health care system stays steady, that is to say the metric won’t change continually.2.Assume that all the statistics we get from the database of the WHO is authentic.3.Assume that the ranking of the world's health systems in 2000 made by WHO isscientific and dependable.4.During the data processing if a data little than x we can replace it with x.5.If there existing data missing for some year’s indicator we can value it with thecorresponding value of the near years.3. SymbolsSymbol Definition and Property'Z The matrix before standardizationZ The matrix after standardizationz j The statistic of the j indicator to each countryu The main component to be evaluatedu m The m th main component of the indicatorl ij The load of the original indicatorR1 The correlation matrix of Zc i The contribution rate of the i th main componentS i The summation of the front i main components’ contribution rateQ The integrated score of each countryX The project set ( the Member States)U The attribute set which also means main component seta ij The attribute value of x i in reference to u jA The decision making matrixa i The mean value of the line I in the primitive matrixb i The standard deviation of row I in the primitive matrix4. Problem AnalysisTo determine several important and viable metrics for assessing the performance of a health care system and comparing health care systems in different countries. We have to know what metrics or indicators are there in a health care system, as is shown in the problem we search the web of the WHO and get the database of the indicators. There exists statistics for 50 core indicators on mortality, morbidity, risk factors, service coverage, and health systems, which take on more than one hundred and fifty terms of raw indicators. We must use some data mining technology or method to distill the crucial metrics.Considering the data is promiscuous and inconsistent and not all the countries have the corresponding data to each indicator from the year 1960 to 2006, we first need to choose certain year’s data as our study object. Then to the mass actual statistical data we can’t expect all the indicators are complete so what to do with the incomplete data to make sure that all the indicators or all the data we used below are universal or effective is an inevitable problem. There are 159 raw indicators how could we select the most important ones and combine them scientifically to make them more useful in measuring quality is another basal problem. Then how could we accomplish this goal? The main components analysis method which we could use to devise our first model will help a lot.Furthermore how could we assess a country’s health care system and make some comparisons with the combined metrics? This situation much agrees with the multiple attribute decision problems. So we could solve this problem by ranking all the countries health care systems using this multiple attribute decision method.5. The Establishment of Model5.1 The Primary Data and Indicators ProcessingAccording to the above problem analysis part we know that we could obtain enough raw data for almost 159 indicators from 1960 to 2006. We first choose a year 2004 whose data is much completer than other years as our study object. Then if some of the indicators of certain country in 2004 have no value and the year close to 2004 such as the year of 2005 or 2003 has the corresponding value we treat this close value as the valve of the country in that indicator in 2004.Based on these we select the indicators that 95% of the country has the corresponding data for them from all the 159 raw indicators. By doing this primary selection we make sure that all the indicators or all the data we used below are universal or effective. After the primary selection we get 48 crucial indicators as our primary outcomes (metrics).5.2 Model 1 DesignFollowing the above analysis we utilize the main components analysis method to devise our first model.When it comes to main components analysis the biggest effect to it is the dimension of the data. So in the practical application we first should make standardization to the data.Assume that 'Z is the matrix before standardization Z is the matrix after standardization z j is the statistic of the j indicator to each country; u is the main component to be evaluated, so the objective function could be:11111221221122221122p p p p m m m mp p u l z l z l z u l z l z l z u l z l z l z =++⎧⎪=++⎪⎨⎪⎪=++⎩""""""" (1)Where u 1, u 2,… u m is called the 1st, 2nd, … mth main component of the indicator z 1, z 2, z p ; l ij is the load of the original indicator z j (j=1,2, …,p) in each main component.The detailed process of this solution is as follows:Step1: Evaluate the standardized matrix Z of the matrix'Z The standardization of the 'Z is just replace the (i=1,2, …,p) and the z 'i z ijof the matrix 'Z with z i (i=1,2, …,p) and with z ij respectively, which is shown in table1Step 2: Evaluate the correlation matrix R1 of matrix ZR1 could be evaluated by the following matrix:1112121222121p p p p pp r r r r r r R r r r ⎡⎤⎢⎥⎢=⎢⎢⎥⎢⎥⎣⎦""##"#"⎥⎥ (2) Where r ij (i, j =1, 2,…,p) is the original indicator z i and z j ’s correlation coefficient specially r ij =r ji . r ij could be derived by the following formula:ij r = (3) Step 3: By formula 1 we can compute the characteristic root and characteristic vector of matrix R1 then rank the characteristic values of R1 as expression 110I R λ−= (4)(5)0≥≥≥≥λλλ"p 21Step 4: Evaluate the contribution rate and the accumulative contribution rate according to formula 1and 1of the main components, determine the proper number of the main components.(6) λ∑==="1(1,2,,i i p k k c i λ)pWhere is the contribution rate of the i th main component.i c(7) S i λ∑p λ====∑"11(1,2,,)i k k i p k k Here is the summation of the front i main components’ contribution rate. If thevalue of the accumulative contribution rate reaches to more than 80% we can approbate the effect of the main components.i S Step 5: Compute the load of the main component l ij(y ,z )(,1,2,,)ij i j ij l p i j p ===" (8)Step 6: Sum up the above five steps get our objective function11111221221122221122............p p p p m m m mp u l z l z l z u l z l z l z u l z l z l z =+++⎧⎪=+++⎪⎨⎪⎪=+++⎩"""p Step 7: Evaluate the integrated value of each country and make a ranking of them with the formula 1.112211(m m m ii )u u λλλλ==+++∑"Q u (9) Q is the integrated score of each country.5.3 Model 2 Design5.3.1 The Description of the PrincipleThe method of the multiple attribute decision making based on dispersion maximization is used to solve the multiple attribute decision making problems with the uncertain weight attribute. We can use this method to make ranking and comparison between different projects with multiple attributes.In more details the smaller the difference between certain attribute for all the projects is the less affection it has on the decision making and ranking of the projects. On the contrary the bigger it is the more affection it has on the decision making and ranking. As a result in the view of ranking the bigger of one attribute’s deviation is the bigger weight of this attribute should be given. Especially if there is no deviation for certain attribute to all the projects which means that this attribute will have little affection on the ranking we can value a zero to its weight.5.3.2 Model DevelopmentStep 1.Structure and Normalize the Decision Making Matrix1.1 Structure the Decision Making MatrixAssume that:M={1,2,…,m},N={1,2,…,n} (10)The projects set which also is the set of the Member States in WHO is XX={x 1,x 2,…,x n } (11)The attribute set which means main component set here is UU={u 1,u 2,…,u m } (12) is the attribute value of in reference to so we obtain the decision making matrix ()ij n m A a ×=whose form is shown as table 2 Table2. The form of the decision making matrixu 1u 2… u m x 1a 11a 12… a 1m x 2a 21a 22… a 2m ## # # x na n1a n2… a nm5.4 Model 3 DesignModel 3 is our predictive model, from model 1 we can get the objective function with the data of that year.When it comes to predicating for the convenience of evaluating the main components we can change the main component which is expressed by the standardization indicator z i into the form that expressed by nonstandard indicator z i ’ to predicate the main components.''''''1111122110''''''2211222220''''''11220(13)............p p p p m m m mp p m u l z l z l z l u l z l z l z l u l z l z l z l ⎧=++++⎪=++++⎪⎨⎪⎪=++++⎩"""Here(14)'()/i i i z z a b =−i Substitute into formula 1 can we obtain the formula 2.'i z i a is the mean value of the line i in the primitive matrix; is the standard deviationof row i in the primitive matrix.i bThen utilize the formula 9 to compute the synthetic score and get variability ofthe system.Normalize the Decision Making MatrixThere are many types of attributes such as benefit type, cost type, fixation type, deviate type, interval type, deviate interval type etc. In our model all the attribute could be sorted to two types the benefit type and the cost type approximately. The benefit cost requires the value of the attribute as big as possible; the cost type requires the value of the attribute as small as possible.To eliminate the impact of the different dimensions to the decision making result we should normalize the decision making matrix A whose values could be obtained from the model 1.Assume that I i (i=1, 2) stands for the subscript set of the benefit type and cost type. If the attribute is benefit type we value i in I i as 1. If the attribute is cost type we value i in I i as 2.1min(),,max()min()ij ij i ij ij ij ii a a r i a a N j I −=−∈∈ (15)2max(),,max()min()ij ij i ij ij ij i i a a r i a a N j I −=−∈∈ (16)After this step we get the normalized matrix ()ij n m R r ×= whose form is the samewith the matrix A.Step 2: Calculus the optimization weight vector w11111,n n ji kj i k j m n nij kj j i k r r w r r =====−=−∑∑∑∑∑j M ∈ (17) Where w j is the j th main component’s weight.Step 3: Computer the synthetic attribute z i (w) (i ∈N) of project x i .1(),,mi ij j j z w r w i N j ==∈∑M ∈ (18)Step 4: Make ranking and comparison to the projects (countries) using z i (w)(i ∈N)6. Applying the Model1 and Model 26.1 Applying the Model 1 to the Statistics of the Year 20046.1.1 Data for Model 1 in the Year of 2004We first select the indicators that 95% of the countries own these indicators from all 159 indicators getting 28 indicators which could be seen in appendix Ⅰ. Then weselect the countries that have the data for all these 28 indicators from all 193 Member States getting 163 countries. By doing these we have made good preparation for our model 1’s solution.6.1.2 Solution of the Model 1 for the Year of 2004Based on the above data we solve our model 1 in matlab using the function of zscore to normalize the data, and then we calculate the characteristic roots and characteristic vector. The characteristic roots are shown in the table 3.Table3. Part Valves of Model 1From the table 3 we can see that the front six red colored components’ accumulative contribution rate reaches to 81.5% which means that most of the main components are involved, so these six components are just our combined metrics. We renamed these six combined indicators with A, B, C, D, E, F metrics all of which are constituted by several raw indicators and could reflect certain performance of a health care system.In more detail the metric A is much positively related with life expectancy, per capita total expenditure on health at international dollar rate etc and much negatively related with mortality rate, incidence of tuberculosis (per 100 000 population per year) etc. The visual relationship between the metric and the 28 indicators is shown in figure 1. The x axis is the order of the 28 indicators which maps to corresponding 28 indicators in appendix Ⅰ. The y axis is the affection of each of the 28 indicator on metric A. All the rest five figures follow this instruction so we won’t explain the rest five figures again.Figure1. The affection of the 28 indicators on metric A The metric B is much positively related with expenditure on health, disease detection rate etc and much negatively related with government expenditure on health, alcohol consumption etc.Figure2. The affection of the 28 indicators on metric B The metric C is much positively related with General government expenditure on health as percentage of total expenditure on health, immunized with disease etc and much negatively related with private expenditure on health as percentage of total expenditure on health, population (in thousands) total etc.Figure3. The affection of the 28 indicators on metric C The metric D is much positively related with private expenditure on health as percentage of total expenditure on health, immunized with disease etc and muchnegatively related with General government expenditure on health as percentage of total expenditure on health etc.Figure4. The affection of the 28 indicators on metric D The metric E is much positively related with external resources for health as percentage of total expenditure on health etc and much negatively related with tuberculosis: DOTS case detection rate, probability of dying (per 1 000 population) between 15 and 60 years etc.Figure5. The affection of the 28 indicators on metric E The metric F is much positively related with immunized with disease, out-of-pocket expenditure as percentage of private expenditure on health etc and much negatively related with general government expenditure on health as percentage of total government expenditure, population (in thousands) total etc.Figure6. The affection of the 28 indicators on metric FThe above descriptions show that our metrics is reasonable, moreover all the 28indicarors we selected could be found in 92% of all Member States, which means that our metrics could be easily collected.Furthermore we get the ranking for all the 163 countries that own orbicular and effective data. The front and the back 20 countries in our ranking and their scores calculated by our model 1 are listed as table 4:Table4. Part of our Ranking by Our Model1The whole ranking is shown in appendix Ⅱ.After obtaining the six metrics we treat the still missing value’s indicator as zero then recompute the ranking of the year 2004 with the model 1 and get another ranking for all the Member States which we list in appendix Ⅲ.In conclusion we put forward 28 important indicators from all the 159 indicators furthermore we combine the 28 important indicators getting 6 main components which we renamed as metric A, B, C, D, E and F. Then we assess the health care system with these six metrics and make a ranking of all the 163 countries.6.1.3 Applying model 1 to the Statistics of the Year 2000Using model 1 and the six metrics obtained from 4.11 we assess the health care system of each country in the year of 2000. This time we only utilize the data of this year, which means that we just substitute the data of 2004 with that of 2000.By doing this we get the ranking of this year as table 5 which just show out the front and the back 20 countries too.Table5. The Part Ranking of the Year 2000 by Model 1 (There are 194 Member States in 2000; the score here is just a relative value computed by our model; the whole ranking is shown in appendix Ⅳ)6.2 Applying the Model 2The six metrics obtained from model1 is ordered. Although the six metrics keep the same in the model 2 as what they are in model 1 according to our assumptions, there is no certain order among them in our model 2. The data processing methods for the raw data are the same with what we have described and used before.6.2.1 Applying the Model 2 to the year of 2004With the help of the software matlab we realize the algorithm of dispersion maximization computing the weight of the six metrics, and then we calculate the synthetic score of each of the 163 country that own holonomic statistics after our data mining process. After comparing the synthetic score of these countries we get the ranking of their health system as shown in table 6.Table6. Part of the ranking for the 163Member States(The score here is just a relative value computed by our model; the whole ranking is shown in appendix Ⅴ)6.2.2 Applying the Model 2 to the year of 2000Similar to 4.1.2 we just replace the data of 4.2.1 with the data of the year 2000 then compute the synthetic score of all the 194 Member States. After comparing the different countries we get the ranking as table7.Table7. Part of the ranking for 2000 by model 27. Comparisons7.1 Comparisons between Different RankingsFrom the above solution we obtain 4 different rankings. The precise clues of our models have already showed their validity. Besides we can load a ranking for all the 190 Member states in 2000 from the WHO’s official web which we list in appendix Ⅷ.By making comparisons between our two rankings with the official ranking of the year 2000 we can test the reliability and practicability of our model to a certain extent. The figure7 shows their relationship clearly.Figure7. The corresponding relationship between our rankings and the WHO’s We can see that the dots which stand for parts of the countries in the rankings match quite well with each other in the three polygonal lines. That means the model1 and model 2’s results not only agree with each other but also agree with the official results quite well. So we can conclude that our two models are practical and reasonable.Since the solution for the year of 2000 is dependable, we have reason enough to predicate that our solution for 2004 is authentic as the only difference between 2004 and 2000 is the substituted statistics and the data of 2004 is more holonomic than that of 2000.To make sure that both of our models’ results for the year 2004 are unitive we make a comparison between their rankings. We select some characteristic countries in both rankings and compare those countries rankings as shown in figure 8.Figure8. The comparison between the two rankings for the year 2004From the figure we find that the two rankings match quite well.In conclusion our models are scientific and our results are authentic.7.2 Comparisons between US and FranceIn the 2000’s ranking of WHO France takes the first place, which also could be seen clearly in the appendix. In the year 2004 there is no official ranking so we assess these two countries health care system with our model 1 to see which country has the better health care system then.The table8 shows their score according to our six metrics:Table8. The comparisons between US and France’s health system according to our metricsThe metric A, C, F belongs to benefit type and the rest belong to cost type. Base on this we can see that the health care system of US in 2004 is better than France in metric A, B, D. According to our table 1 we know that the synthetic score of US is better than France.7.3 Comparisons between US and IndiaIn the ranking of WHO the health system of US is better than India. With the help of our mode 1 we consider that India has the poor health care system in 2004, so we make a comparison between them.Table9.The comparisons between US and India’s health system according to our metricsSimilar to 7.2 we can see that the health care system of US is better than India in metric A, B, C, D, F. Also from the table 9 we know that the ranking of US is much better than India.8. Applying the Model 3Based on model 1 and model 2 with the help of the software matlab we realize the algorithm in model 3 and get the predictive function of the synthetic scores as follows:''''1234'7''''67891''''1112131410.0033810.00966920.0105590.00194680.000522790.00052406 3.06100.0782130.00421940.0003620.00873510.00956890.00984890.000392260.0043929Q z z z z z z z z z '5z z z z z z −=−++−−−−×−−−−++−+'5''''1617181920''''2122232425'''2627280.0053440.0257520.00377190.000143460.000182770.000109410.000136790.00534410.056440.00292340.000840420.029650.012447 3.8668''z z z z z z z z z z z z ++−++++−+−−++−z (19)Considering the affection of the weight on the synthetic score we could find that the bigger the absolute value of weight is the bigger the impact is on the synthetic score of the country. On the contrary if the absolute value of weight is small then the variation of the metric won’t produce big changes to the synthetic score. Then we take some indicators of the all 28 indicators as examples to discuss what affection it will has on the health care system if the various changes are occurred.'8z is the formula is the total fertility rate (per woman). It has a negative correlation with the synthetic score. What’s more it has a big affection on the score so this indicator should be as small as possible, which means that the government should take some measures to control the population within a proper range to improve the health care system of the nation.'24z is the total expenditure on health as percentage of gross domestic product. Itis an indicator that positively related with the synthetic score which means that the more it spend on the total expenditure on health as percentage of gross domestic product the better score it has in the system.'17z is the general government expenditure on health as percentage of totalgovernment expenditure. It is an indicator that positively related with the synthetic score which means that the bigger the general government expenditure on health as percentage of total government expenditure is the better score it has in the system'3z is the life expectancy at birth (years) males. It is an indicator that positivelyrelated with the synthetic score which means that the longer the life expectancy at birth (years) males is the better score it has in the system.'z stands for the neonatal mortality rate (per 1 000 live births). It has a negative 11correlation with the synthetic score. That’s to say the smaller the neonatal mortality rate (per 1 000 live births)is the better the health care system will become.9. The Strength and Weakness9.1The StrengthWe obtain the statistics directly from the raw database of the WHO’s official web not from the report of the WHO. We use some data mining technology to draw the available and effective data from thousands terms of data ourselves.We develop three different models to solve all the six parts of the problem, those models are built with precise logic, scientific principle which could solve the problems efficaciously.We don’t solve the problem part by part but solve them in our models’ development and solution process, which keeps the whole paper’s with a good continuity.We compare our result with the practical result, which tests our models’ practicability and validity greatly.Our models could be easily extended to other fields to solve the multiple attribute decision making problems.Our models are independent to the metric (indicators) to a certain extent as the algorithm of our models has the universal applications.9.2 The WeaknessThe raw data we get is the data from the real world, which means that there must be some imperfect data which do have some negative impact on our result.As there are so many indictors that it is hard to select proper metrics to assess the health system properly without some kind of error.Because the limitation of the time and resource it’s inevitable to have some imperfect aspects in our models, analysis and paper.10. References[1] Zeshui Xu, 8/2004, Uncertain Multiple Attribute Decision Making: Methods andApplications, Tsinghua University Press.[2] Qiyuan Jiang, Jinxing Xie, 12/2004, Mathematical Model, Higher Education Press[3] The World Health Report 2000 - Health systems: improving performance.http://www.who.int/whr/2000/en/whr00_en.pdf[4] World Health Organization, http://www.who.int/research/en/s[5] Principal Component Analysis,/jpkc/jldlx/admin/ewebeditor/UploadFile/200783101241734.ppt[6] /wiki/World_Health_Organisation,"World Health Organization"11. AppendixAppendixⅠ: The list of all the 28 indicators and their sequence numberAppendixⅡ: The Ranking of all 163 Countries in 2004 by model 1Appendix Ⅲ: The Ranking of all 194 Countries in 2004 by model 1。

Winter is approaching, may the dragon’s wings grow moreabundantSummaryIn the game of thrones, Daenerys Targaryen depicts the image of a dragon. In eastern and western cultures, the phenomenon of dragons is not uncommon. If dragons live in modern society, how can we raise these war monsters? Research, and applied the cross disciplines of biology, physics, and chemistry to build a mathematical model and solve it to achieve the maximum growth of the dragon. Of course, dragons do not exist in real life, so we likened pterosaurs, modern Aircraft and chemical burner to derive the specific physiological characteristics of the dragon to ensure the rationality and scientificity of the research.First, we studied the flight and fire-spitting models of dragons. Through analogical reasoning, our hypothetical dragon's fire-spitting principle is similar to modern alcohol flamethrowers. For dragon flight, we used fluid mechanics to get the dragon's flight speed. And glucose energy loss. Combining the two to get the energy loss model of the dragon. Second, we studied the basic physical characteristics of the dragon. For the relationship between the body length and body age of the dragon, we established an elastic model of growth. Because the weight and body length of dragons have upper and lower limits, in order to comply with basic ecology, we have defined the dragon's bone saturation value as the cut-off value, and conducted a segmented study. When studying the relationship between weight and body length, We know that the weight of the dragon is proportional to the cube of the body length. Then, because the dragon needs resources to replenish like other animals, we built a dragon's food supply model. Suppose that the three dragons have the same competitiveness and the daily sheep Resources are the same. According to ecology, when the number of sheep in a certain area reaches k / 2, we need to migrate the dragon. Finally, the temperature will affect the living environment of the dragon, so the dragon needs to followMigration was selected for changes in temperature, and we selected three areas of drought, cold, and warmth to study the dragon, and integrated the model of the regional area of the dragon by the appealing model.In addition, we wrote a letter to the author of the Song of Ice and Fire, giving some suggestions on the actual ecological foundation of the dragon, hoping to be adopted. Although the dragon does not exist in our real life, the dragon can be broken down into Part of our modern society. For the dragon's flying spitfire energy loss model, we can further study the aircraft's fluid mechanics and modern flamethrowers. The study of non-existent organisms also prepares us for the arrival of new species .table of ContentsWinter is approaching, may the dragon’s wings grow more abundant (1)Summary (1)table of Contents (2)1 Introduction (3)1.1 restatement (3)1.2 Problem Analysis (3)2 Assumptions and reasons (4)3 Symbol Definition (4)4. Mathematical modeling (5)4.1 About Dragon Flight and Spitfire Consumption (5)4.2 About the relationship between dragon's body length and weight and age (7)4.3 About Dragon's Food Supply (8)4.4 Regulating the area of dragons by region (9)5 Sensitivity analysis (10)6 Model evaluation and outlook (11)6.1 Model evaluation (11)6.2 Further discussion (12)7 to a letter from George RR Martin (12)8.Appendix: (13)8.1 References (13)8.2 Matlab code (13)1 Introduction1.1 restatementIn the magical TV series "Game of Thrones", Daenerys Targaryen, known as the Mother of Dragons, raised three dragons as an aggressive army. Dragons have always been the most mysterious monsters in Eastern and Western cultures, but if Dragons live in the present era, how should we feed the three dragons in pursuit of maximum growth? In this article, we assume that the growth rules of dragons are in line with basic biology. To study them, we build mathematical models to solve problem.a. Analyze the change of the dragon's weight length with age, and estimate the value of the dragon's weight length corresponding to the age group.b. Investigate the loss of self energy during dragon fire, flight, and breathing, so as to estimate the minimum supply value of dragon for external activitiesc. Dragons need food and survival areas like other animals in the real world. Through certain assumptions and calculations, we can determine the total amount of food that dragons need daily and the size of living areas in three areas.d. Sensitivity analysis: As temperature and climate change, dragons will also migrate to different regions. Therefore, we need to analyze the differences in the impact of dragons on the survival of arid regions, temperate regions, and cold regions.1.2 Problem AnalysisBecause dragons do not exist in real life, we need to use some things in the real world to compare dragons in order to achieve the purpose of studying dragons. In analyzing the biological morphological characteristics of dragons, we use the knowledge of ecology and basic elements of biology Let's conceive the basic biological characteristics of the dragon such as weight and body length. For the energy loss model of the dragon, we have studied three aspects to describe its loss. Here we compare the modern flamethrower and establish related chemical equations to achieve the research of the dragon. Spitfire loss. In addition, in TV series such as "Game of Thrones" we will find that dragons can fly in common sense, so we have derived the dragon's flight loss. Of course, all aerobic organisms can breathe. Dragons are no exception, so there is a loss of breathing to maintain body temperature. At the same time, in order to make up for the loss of dragons in daily activities, we have established a material reserve model, in which materials are cattle and sheep in real life, etc. Finally, during the cyclical changes in climate and food, the dragons we feed will also migrate to some extent, so we analyzed the impact of different regions on the growth of dragons.Into account various factors that we can more scientific training of dragons, have achieved our purpose.2 Assumptions and reasonsAfter a comprehensive analysis of the problem, in order to increase the enforceability, we make the following assumptions to ensure the rationality of our model establishment.2.1 Assumptions: The basic biological characteristics of dragons are in line with the law of biological growth. In modern life, the growth and development of dragons should also be similar to other animals and conform to basic biology.2.2 Assumption: The dragon will spit fire and fly, and its flight conforms to the physical environment of fluid mechanicsReason: In Game of Thrones, the image of the dragon was once able to fly and spit fire.2.3 Assumption: In the single field we are studying, the environment of a certain area will not change abruptly and maintain a dynamic stability.2.4 Hypothesis: Dragons are top predators in the food chain, but dragons do not cause devastating harm to the biosphere.2.5 Assumption: The weight distribution of the dragon is uniform, and the body length reaches 30 to 40 cm at the time of birth.Reason 2.6: We refer to ancient biology and some dinosaur fossils.2.7 Hypothesis: Except for the skull, heart, liver, lungs, kidneys, bones, etc., the sum of other body masses is proportional to the cube of height.Reason: The hypothesis is obtained by counting the relationship between body length and weight of modern organisms.2.8 Hypothesis: The dragon is a constant temperature animal whose body temperature is not affected by external factors.Reason: A few pterosaur fossils have traces of "hair" on the surface, while the dragons in Game of Thrones are similar to pterosaurs.2.9 Hypothesis: The dragon is fully aerobic during the flight to provide energy2.10 Hypothesis: A certain fixed ratio of the amount of energy that is not assimilated by the growth and metabolism of the dragon's breathing and other organisms2.11 Hypothesis: Dragon's Flight Similar to Modern Fighter3 Symbol Definition4. Mathematical modeling4.1 About Dragon Flight and Spitfire Consumption4.1.1 Proposed modelConsidering that dragons fly and spit fire during activities, we have established an energy loss model. Comparing the principle of dragon's spitfire with modern flamethrowers, modern flamethrowers consume hydrocarbons or alcohols. It does not cause any impact, so the dragon's fire-breathing principle is in line with the alcohol flame-thrower principle. Considering that the formaldehyde produced by the metabolism of methanol in the animal body is harmful to the body, we stipulate that ethanol is the fuel used by the dragon's flame. In the process, the relationship between the dragon's flight speed and glucose energy consumption is obtained according to fluid mechanics. In this process, we assume that the aerobic respiration is completely performed, and the energy consumed by the dragon due to flight is obtained according to the glucose consumption. In summary, the dragon energy loss model is obtained. .4.1.2 Establishment and Solution of Dragon's Spitfire ModelThe thermochemical equation for ethanol combustion is: C2H5OH (l) + 3O2 (g) = 2CO2 (g) + 2H2O (l) △H = -12KJ / gSpecify the energy released per unit mass of ethanol combustion x1When the dragon spit fire in unit time t, the unit mass of ethanol consumption is a fixed valueThe energy consumed by the fire time t1 is w1The mass consumed by the fire time T1 is m4Let the energy emitted by the combustion of unit mass of ethanol be w1 'Then W1 = x1 * tm4=W1/W1’Solve m4 = x1 * t / W1 '4.1.3 Establishment and Solution of Dragon Flight ModelDuring the flight of the dragon, it will be affected by the air resistance. In the ideal situation, the dragon's flight can be considered as a uniform acceleration and then a uniform speed, and it will decelerate when it is about to reach its destination.When Long uniform acceleration is specified, the acceleration is aSince the flight of the dragon is similar to that of a fighter, a = 30m / s ^ 2The speed of the dragon during uniform motion is v0The total flight length of the dragon during flight is sBecause air resistance is proportional to the speed of movement, that is, F1 = k * v (where k is a constant)Since the dragon's flight is similar to an airplane, we can get k = 3.2325Available according to the relevant kinematic formulaThe flying distance of the dragon during uniform acceleration is s1 = (v0) ^ 2 / 2aThe flying distance of the dragon during uniform deceleration is s3 = (v0) ^ 2 / 2aThe flying distance of the dragon during uniform motion is s2 = s-s1-s3Average air resistance during uniform acceleration F1 '= k * (0 + v0) / 2The average air resistance during uniform motion is F1 '' = k * v0Average air resistance during uniform deceleration f1 '' '= k * (v0 + 0) / 2According to the law of conservation of energyThe energy w2 consumed by the dragon during flight is all used for air resistance workW2=F1’*s1+F1’’*s2+F1’’’*s3Solve W2 = 3.2325 * v0 * s-3.2325 * (v0) ^ 3 / (2 * 30)During the flight of the dragon, the principle of energy provided by aerobic respiration isC6H12O6+6O2=6CO2+6H2OAmong them, the energy produced when 1g of glucose is completely consumed is 16KJThen the weight consumed in this process is m6 = W2 / 16[v,s]=meshgrid(0:0.1:100;0:0.1:100);m=3.2325*v*s-3.2325*v^3/60mesh(v,s,m)4.2 About the relationship between dragon's body length and weight and age4.2.1 Proposed ModelFirst, in order to study the relationship between the weight, length, and age of the dragon, that is, morphological characteristics, we established a model of elasticity during growth. The above-mentioned change curve is continuous, so we use the weight of the dragon at birth, and consider the weight and length of the dragon. The relationship between age changes can be used to derive the normal weight and body length of dragons in all ages. When analyzing the weight changes of dragons, biological knowledge shows that the amount of assimilation of the dragon is equal to the intake amount minus the amount of unassimilated amount Considering that the growth rate of the dragon in adulthood is a watershed, we use the saturation value of the dragon's head, heart, and liver as a cutoff value to estimate the relationship between the dragon's weight and age, respectively. When studying the body length of the dragon, according to the existing morphological knowledge, the head to hip of the dragon is used as the length standard. Because the weight of the dragon is proportional to the cube of the dragon's length, we get the weight and length Functional relationship. Of course, the daily weight gain of the dragon must be less than the daily energy consumption. In summary, we have a dragon intake model.4.2.2 Model establishmentSpecify the weight of the dragon as mDragon was born with a weight of m0 (known m0 = 10kg)Assume that the mass of cattle and sheep fed by a train every day is m2The assimilation amount of the dragon is fixed at a%A certain fixed ratio of the amount of unabsorbed energy due to growth and metabolism of organisms such as dragon's respiration, recorded as b%The weight gain of the dragon is m 'The sum of the weight of the dragon's head, heart, liver, lungs, kidneys, bones, etc. m1 increases with age y until adulthoodDragon is y1 when he is an adultThe growth rate of m1 is v1The mass of m1 at birth is m0Before the dragon reaches y1m1=m0+v1*yAfter the dragon reaches y1m1’=m0+v1*y14.2.3 Model Solvingm’=m2*(1-a%)*(1-b%)-m4-m6So the weight of the dragon m = m '+ m0Except for the dragon, except for the head, heart, liver, lungs, kidneys, bones, etc., the sum of other body masses is proportional to the cube of height, and the body length is recorded as l When the age of the dragon does not reach y1, l = (m-m1) ^ (1/3)When the age of the dragon reaches y1, l '= (m-m1') ^ (1/3)M2 =y=0:0.1:20function[y]= (m2*(1-a%)*(1-b%)-m4-m6-v1*y)y=20:0.1:100function[y]= (m2*(1-a%)*(1-b%)-m4-m6-v1*20)power(y,1/3)4.3 About Dragon's Food Supply4.3.1 Proposed modelBased on the above analysis, we studied the living area of the three dragons in the region andtheir impact on the ecological community in the region. For the sake of research, we assume that the other creatures in the region are cattle and sheep, and the competitiveness of the three dragons is comparable, Being a top predator in the food chain.4.3.2 Model establishmentThe local food chain can be approximated as: grass → cow or sheep → dragonAssume that the weight of the grass in the arid region, the warm temperate region, and the Arctic region is the same as m8.Remember that the mass of each cow and sheep is the same as m7We provide the same initial number of cattle and sheep in all three regionsAssume that the daily growth rate of cattle and sheep is c%The initial number of cattle and sheep is n1And n1 is the number of populations reaching k in the regionDragons live in this area. When the number of cattle and sheep reaches k / 2, in order to ensure the balance of the ecological environment, the dragons need to be moved to other regions.4.3.3 Model SolvingThe initial amount of cattle and sheep on day 1 is: n1The initial amount of cattle and sheep on the second day is: N2 = (N1-3 * m2 / m7) * (1 + c%) The initial amount of cattle and sheep on the third day is: N3 = ((N1-3 * m2 / m7) * (1 + c%)-3 * m2 / m7) * (1 + c%)……From this we can get the initial amount of Ni of cattle and sheep on day iI can be solved by the equation Ni = K / 2That is, the dragon needs to change a living area after living in the area for i days.4.4 Regulating the area of dragons by region4.4.1 Proposed modelIn order to ensure the normal growth of the dragon, we provide fixed-quality cattle and sheep as the supply of resources for the survival of the dragon region, and assume that the number of cattle and sheep is proportional to the size of the regional living area. Considering the growth rate of cattle and sheep, we have established a differential The equation draws the relationship between the growth rate of cattle and sheep and the age of the dragon. However, cattle and sheep will reach a growth saturation value at a certain moment, we will consider it in segments to ensure that the data is more scientific. In order to comply with ecology, cattle The supply of sheep should also have a lower limit. In summary, we have established a dragon-cow-sheep-living area function model.4.4.2 Model establishmentRemember that the assimilation rate of cattle and sheep grazing in this area is d%Because the solar energy received by the surface area of the three areas is different, the total area required for the grass under the same quality conditions is different. The utilization rate of the solar energy is required to be e% (0.5 <e <1 under the natural conditions of the search data)The solar energy per unit area in the arid area is q1Unit area solar energy in warm zone is q2Solar energy per unit area in the Arctic is q34.4.3 Model SolvingAccording to the utilization of solar energy, we can find:Area required to support the arid areas where the three dragons live: S1 = m8 / (q1 * e%)Warm zone: S2 = m8 / (q2 * e%)Arctic region: S3 = m8 / (q3 * e%)5 Sensitivity analysisImpact of climatic conditions on dragon lifeThe effect of climatic conditions on dragon growth can be obtained from the logistic growth model dm/dt=r*m*(1-m/k)That is m = 15 / (4 * t + 20);(Where m is the mass that the dragon can eventually grow into)Where m0 = 10 (k is the maximum carrying capacity of the ecosystem and r is a parameter of the environmental carrying capacity)k is 0.75r is 0.8dm/dt=0.8*m*(1-m/0.75)t=0:0.1:100;m=15./(4*t+20);plot(t,m)6 Model evaluation and outlook6.1 Model evaluationFor the idealized model of Yanglong, we have performed various aspects of modeling and solving, and the scope is relatively broad. Of course, the content has been streamlined to facilitate understanding and application. We have used physical and biological models based on The mathematical formulas are also encountered in the middle school stage. In these more basic models, we have solved efficiently, and at the same time, for the interdisciplinary problems of question a, we have considered the field that the ideal biology of dragons may involve and solve The process is relatively complete. In addition, the four models are closely related and logical. First, we consider the consumption of dragons in daily life, and use the results of consumption to calculate the weight and length of the dragon at various ages. In order to meet the requirements of all ages, we have established the ecological supply model of dragons, and discussed the problem of periodic alternating fields. Second, the fields are also scoped. Therefore, we calculated the scope of three areas with different climates. Interval problems. However, the models we build are idealized, the data is also streamlined, and the assumptions set are also fallible. In reality,The data is diverse and complex, and our considerations are obviously lacking, and further optimization is needed in the later stage. In summary, the model we built is very consistent with the solution of the problem. Although there are some flaws, it does not affect the specific Specific analysis of the problem.6.2 Further discussionCombining the models and evaluations described above, we will improve in the later stages. If this model is used in a specific environment, by statistic large amounts of real data, we can optimize the model. At the same time research also It will be more scientific and rigorous, and it will be more efficient for raising a fictional creature.7 to a letter from George RR MartinDear George RR MartinHope you are wellAfter reading the Song of Ice and Fire, we watched the "Game of Thrones". We became very curious about the mysterious giant that appeared in it-the dragon. Dragons are not uncommon in Eastern and Western cultures. In previous impressions However, there are few studies on dragons. So if we imagine that dragons live in modern times, what would it look like?According to the description of the dragon in the novel, we discussed the following questions. What are the ecological impacts and requirements of the dragon? What is the energy consumption of the dragon, what are their calorie intake requirements? How much area is needed to support the three dragons? Energy loss during fire? In response to these problems, we constructed a multivariate non-linear objective programming model of dragon's growth index and function, size, diet, growth changes, and other animal-related features. Considering the physical characteristics of dragons, we will Its fire-spitting ability is analogized to modern flame-throwers to ensure scientific and rational research.Based on these, we have established a mathematical model. The weight and length of the dragon also grows with the age of the dragon. When the dragon grows slowly at the initial 10 kilograms, the mass of sheep it needs each year also varies The growth of the supply chain of resources and the size of the ecological community should also change. The fire and flight of the dragon will also have a certain impact on the ecological environment. As the dragon and other creatures will migrate with changes in temperature, we choose The three regions of the cold zone, temperate zone and arid zone were taken as key research objects to find out the impact of climate change on Long.Therefore, we make the following suggestions, hoping that the survival of the dragon in the realm of science is more reasonable and scientific.When the herd resource is saturated, the dragon needs to expand the area living area.Dragons like warm, hydrated areas, and migrate to warm areas in the cold winter.A dragon has a certain weight and length when it is just born, and it will grow over time, but it also has an upper limit. It cannot grow endlessly.The daily energy intake of the dragon is limited, and the dragon spitfire flight consumes energy, which requires that the dragon's flight distance and spitfire time are limited, and it is related to the age of the dragon body.Because the living conditions of the three areas are different, the unit area will also receive solar energy differently, resulting in different resource distributions in each area, which means thatthe speed of dragon growth should also be different in different areas.The environmental carrying capacity of each area is limited, and the dragon does not stay in one place for long.The above content is the result of our research on the Queen of Dragons. We sincerely hope that you can adopt it, and we have been looking forward to your new book.Your fans: 27 groupsJanuary 7, 20208.Appendix:8.1 References1) Chen Yun.Research on Environmental Carrying Capacity of Yuhuan County [j] .Energy and Energy Conservation, 2014 (4): 31-33.2) Zhu Ziqiang.Aerodynamic design of modern aircraft [m] .Beijing: National Defense Industry Press, 2011-10-13) Jin Lan.Environmental Ecology [m] .Higher Education Press, 19928.2 Matlab codeModeling the flight of a dragon[v,s]=meshgrid(0:0.1:100;0:0.1:100);m=3.2325*v*s-3.2325*v^3/60mesh(v,s,m)Sensitivity Analysis of the Impact of Climate Conditions on Lifet=0:0.1:100;m=15./(4*t+20);plot(t,m)。

标准的数学建模论文范文(合集18篇)【摘要】文章阐述了我们应用数学的发展现状，分析了应用数学建模的意义，提出在应用数学中渗透建模思想的措施，以期能够对当前应用数学建模思想的发展提供参考。

【关键词】应用数学;数学建模;建模思想将建模的思想有效的渗透到应用数学的教学过程中去，是我们当前开展应用数学教育的未来发展趋势，怎样才能够使应用数学更好的服务社会经济的发展，充分发挥数学工具在实际问题解决中的重要作用，是我们当前进行应用数学研究的核心问题，而建模思想在应用数学中的运用则能够很好的解决这一问题。

1当前应用数学的发展现状以及未来发展趋势2开展数学建模的意义数学这一学科不仅具有概念抽象性、逻辑严密性、体系完整性以及结论确定性，而且还具备非常明显的应用广泛性，伴随着计算机网络在社会生活中的广泛运用，人们对于实践问题的解决要求越来越精确，这就给应用数学的广泛运用带来了前所未有的机遇。

应用数学在这一背景下也已经成为当前高科技水平的一个重要内容，应用数学建模思想的引入与使用能够极大的提升自身应用数学的综合水平以及思维意识，开展应用数学建模不仅能够有效的提升自己的学习热情与探究意识，而且还能够将专业知识同建模密切结合在一起，对于专业知识的有效掌握是非常有益的。

3渗透建模思想的对策措施3.1充分重视建模的桥梁作用3.2将建模的方法以及相关理论引入到数学教学中来我国当前数学课程教学体系的现状包括高等数学、线性代数、概率论与数理统计等几个部分。

当前应用数学的发展，满足这一学科的建设以及其他学科对这一学科的需要，教师在教学中应当将问题的背景介绍清楚，并列出几种解决方案，启发学生进行讨论并构建数学模型。

学生们在课堂上就能够获得更多的思考和讨论的机会，能够充分调动学生们的积极性，使其能够立足实际进行思考，这样一来就形成了以实际问题为基础的数学建模教学特色。

3.3积极参加“数学模型”课等相关课程与活动数学应用综合性的实验，要求我们掌握数学知识的综合性运用，做法是老师先讲一些数学建模的一些应用实例，然后学生上机实践，强调学生的动手实践。

数学建模论文（精选4篇）数学建模论文模板篇一1数学建模竞赛培训过程中存在的问题1.1学生数学、计算机基础薄弱，参赛学生人数少以我校理学院为例，数学专业是本校开设最早的专业，面向全国２８个省、市、自治区招生，包括内地较发达地区的学生、贫困地区（包括民族地区）的学生，招收的学生数学基础水平参差不齐．内地较发达地区的学生由于所处地区的经济文化条件较好，教育水平较高，高考数学成绩普遍高于民族地区的学生．民族地区由于所处地区经济文化较落后，中小学师资力量严重不足，使得少数民族学生数学基础薄弱，对数学学习普遍抱有畏难情绪，从每年理学院新生入学申请转系的同学较多可以窥见一斑．虽然学校每年都组织学生参加全国大学生数学建模竞赛，但人数都不算多．从专业来看，参赛学生主要以数学系和计算机系的学生为主，间有化学、生科、医学等理工科学生，文科学生则相对更少．理工科类的学生基本功比较扎实，他们在参赛过程中起到了重要作用．文科学生数学和计算机功底大多薄弱，更多的只是一种参与．从年级来看，参赛学生以大二的学生居多；大一的学生已学的数学和计算机课程有限，基本功还有些欠缺；大三、大四的学生忙着考研和找工作，对数学建模竞赛兴趣不大．从参赛的目的来看，有２０％左右的学生是非常希望通过数学建模提高自己的综合能力，他们一般能坚持到最后；还有５０％的学生抱着试试看的态度参加培训，想锻炼但又怕学不懂，觉得可以坚持就坚持，不能则中途放弃；剩下的３０％的学生则抱着好奇好玩的态度，他们大多早早就出局了．学生的参赛积极性不高，是制约数学建模教学及竞赛有效开展的不利因素．1.2无专职数学建模培训教师，培训教师水平有限，培训方法落后数学建模的培训教师主要由理学院选派数学老师临时组成，没有专职从事数学建模的教师．由于学校扩招，学生人数多，教师人数少，数学教师所承担的专业课和公共课课程多，授课任务重；备课、授课、批改作业占用了教师的大部分工作时间，并且还要完成相应的科研任务．而参加数学建模教学及竞赛培训等工作需要花费很多时间和精力，很多老师都没有时间和精力去认真从事数学建模的教学工作．培训教师队伍整体素质不够强、能力欠缺，指导起学生来也不是那么得心应手，且从事数学建模教学的老师每年都在调整，不利于经验的积累．另外，学校对参与数学建模教学及竞赛培训的教师的鼓励措施还不是十分到位和吸引人，培训教师对数学建模相关的工作热情不够，缺乏奉献精神．在２０１１年以前，数学建模培训主要采用教师授课的方式进行，但各位老师授课的内容互不联系．比如说上概率论的老师就讲概率论的内容，上常微分方程的老师就讲常微分的内容．学生学习了这些知识，不知道有什么用，怎么用，不能将这些知识联系起来转化为数学建模的能力．这中间缺少了很重要的一个环节，就是没有进行真题实训．结果就是学生既没有运用这些知识构建数学模型的能力，也谈不上数学建模论文写作的技巧．虽然学校年年都组织学生参加全国大学生数学建模竞赛，但结果却不尽如人意，获奖等次不高，获奖数量不多．1.3学校重视程度不够，相关配套措施还有待完善任何一项工作离开了学校的支持，都是不可能开展得好的，数学建模也不例外．在前些年，数学建模并没有引起足够的重视，学校盼望出成绩但是结果并不理想，对老师和学生的信心不足．由于经费紧张，并未专门对数学建模安排实验室，图书资料很少，学生用电脑和查资料不方便，没有学习氛围．每年数学建模竞赛主要由分管教学的副院长兼任组长，没有相应专职的负责人，培训教师去参加数学建模相关交流会议和学习的机会很少．学校和二级学院对参加数学建模教学、培训的老师奖励很少，学生则几乎没有．在课程的开设上也未引起重视，虽然理学院早在１９９７年就将数学实验和数学建模课列为专业必修课，但非数学专业只是近几年才开始列为公选课开设，且选修率低．2针对存在问题所采取的相应措施2.1扩大宣传，重视数学和计算机公选课开设，举办数学建模学习讨论班最近两年，学院组建了数学建模协会，负责数学建模的宣传和参赛队员的海选，通过各种方式扩大了对数学建模的宣传和影响，安排数学任课教师鼓励数学基础不错的学生参赛．同时邀请重点大学具有丰富培训经验的老师来做数学建模专题讲座，交流经验．学院重视数学专业的基础课程、核心课程的教学，选派经验丰富的老教师、青年骨干教师担任主讲，随时抽查教学质量，教学效果．严抓考风学风，对考试作弊学生绝不姑息；学生上课迟到、早退、旷课一律严肃处理．通过这些举措，学生学习态度明显好转，数学能力慢慢得到提高．学校有意识在大一新生中开设数学实验、数学建模和相关计算机公选课，让对数学有兴趣的学生能多接触这方面的知识，减少距离感．选用的教材内容浅显而有趣味，主要目的是让同学们感受到数学建模并非高不可攀，数学是有用的，增加学生学习数学的热情和参加数学建模竞赛的可能性．为了解决学生学习数学建模过程中的遇到的困难，学院组织老师、学生参加数学建模周末讨论班，老师就学生学习过程中遇到的普遍问题进行讲解，学生分小组相互讨论，尽量不让问题堆积，影响后续学习积极性．通过这些措施，参赛学生的人数比以往有了大的改观，参赛过程中退赛的学生越来越少，参赛过程中的主动性也越来越明显．2.2成立数学建模指导教师组，分批培养培训教师，改进培训方法近年来，学院开始重视对数学建模培训教师的梯队建设，成立了数学建模指导教师组．把培训教师分批送出去进修，参加交流会议，学习其它高校的经验，并安排老教师带新教师，培训教师队伍越来越稳定、壮大．从去年开始，理学院组织学生进行了为期一个月的暑期数学建模真题实训，从８月初到８月底，培训共分为７轮．学生首先进行三天封闭式真题训练———其次答辩———最后交流讨论．效果明显，学生的数学建模能力普遍得到了提高，学习积极性普遍高涨．９月份顺利参加了全国大学生数学建模竞赛．从竞赛结果来看，比以前有了比较大的进步，不管是获奖的等次还是获奖的人数上都取得了历史性突破．有了这些可喜的变化，教师和学生的积极性都得到了提高，对以后的数学建模教学和培训工作将起着极大的促进作用．除了这种集训，今后，数学建模还需要加强平时的教学和培训工作．2.3学校逐渐重视，加大了相关投入，完善了激励措施最近几年，学校加大了对数学建模教学和培训工作的相关投入和鼓励措施．安排了专门的数学建模实验室，配备了学院最先进的电脑、打印机等设备，购买了数学建模相关的书籍．划拨了数学建模教学和培训专项经费．虽然数学建模教学还没有计入教学工作量，但已经考虑计入职称评定的相关工作量中，对参加数学建模教学和培训的老师减少了基本的教学工作量，使他们有更多的时间和精力投入到数学建模的相关工作中去．对参加全国大学生数学建模竞赛获奖的老师和学生的奖励额度也比以前有了很大的提高，老师和学生的积极性得到了极大的提高．3结束语对我们这类院校而言，最重要的数学建模赛事就是一年一度的全国大学生数学建模竞赛了．竞赛结果大体可以衡量老师和学生的付出与收获，但不是绝对的，教育部组织这项赛事的初衷主要是为了促进各个院校数学建模教学的有效开展．如果过分的看重获奖等次和数量，对学校的数学建模教学和组织工作都是一种伤害．参赛的过程对学生而言，肯定是有益的，绝大多数参加过数学建模竞赛的学生都认为这个过程很重要．这个过程可能是四年的大学学习过程中体会最深的，它用枯燥的理论知识解决了活生生的现实中存在的问题，虽然这种解决还有部分的理想化．由于我校地处偏远山区，教育经费相对紧张，投入不可能跟重点院校的水平比，只能按照自身实际来．只要学校、老师、学生三方都重视并积极参与这一赛事，数学建模活动就能开展的更好．数学建模论文模板篇二培养应用型人才是我国高等教育从精英教育向大众教育发展的必然产物，也是知识经济飞速发展和市场对人才多元化需求的必然要求。

2023华中杯数学建模A题精品论文来啦华中杯A题完整论文共85页，一些修改说明7页，正文67页，附录11页
从昨晚又是一个通宵到现在，比我预想的出论文时间晚了很多，主要
是我也要保证质量，没做到我满意就不想出。

本题主要就是三个点，差异
性分析、相关性分析再加上分类预测。

思路倒不难，但是特征数据实在过
于多，所以要基于题目要求不断进行数据预处理，另外就是，实际数据与
附件2那个量化表有的是对应不上的，例如满意度数据不是评分而是判断
是否，所以要很繁琐地转换为评分数据。

数据处理也就是繁琐点，相关性
和差异性则是基于附件3细心判断，至于预测模型，无脑机器学习后我不
断调试，最后精度表现都能达到要求，第一问判断的精度在80%多。

之所以篇幅这么长，是因为
我把所有中间过程的数据图表和求解结果都放在了正文里，你们自己
摘到附录。

此外我论文很多黄字提醒用来解释我为什么要这么做，基本就
是手把手教你怎么做，并且我还要照顾每个人的水平，所有会有些地方需
要写得很繁琐，一些中间过程展现得事无巨细，你们自己删减。

实在太累了，还要写华中杯，所以我就不细讲了，具体的讲解大家可
以看我汇总贴里的讲解视频：
放点截图：
大概就这些，具体我到底怎么做的一句两句说不完，请移步讲解视频：。

数学建模优秀论文.doc数学建模比赛预选赛温室中的绿色生态臭氧病虫害防治2009年12月，哥本哈根国际气候大会在丹麦举行之后，温室效应再次成为国际社会的热点。

如何有效地利用温室效应来造福人类，减少其对人类的负面影响成为全社会的聚焦点。

臭氧对植物生长具有保护与破坏双重影响，其中臭氧浓度与作用时间是关键因素，臭氧在温室中的利用属于摸索探究阶段。

假设农药锐劲特的价格为10万元/吨，锐劲特使用量10mg/kg-1水稻；肥料100元/亩；水稻种子的购买价格为5.60元/公斤，每亩土地需要水稻种子为2公斤；水稻自然产量为800公斤/亩，水稻生长自然周期为5个月；水稻出售价格为2.28元/公斤。

根据背景材料和数据，回答以下问题：（1）在自然条件下，建立病虫害与生长作物之间相互影响的数学模型；以中华稻蝗和稻纵卷叶螟两种病虫为例，分析其对水稻影响的综合作用并进行模型求解和分析。

（2）在杀虫剂作用下，建立生长作物、病虫害和杀虫剂之间作用的数学模型；以水稻为例，给出分别以水稻的产量和水稻利润为目标的模型和农药锐劲特使用方案。

（3）受绿色食品与生态种植理念的影响，在温室中引入O3型杀虫剂。

建立O3对温室植物与病虫害作用的数学模型，并建立效用评价函数。

需要考虑O3浓度、合适的使用时间与频率。

（4）通过分析臭氧在温室里扩散速度与扩散规律，设计O3在温室中的扩散方案。

可以考虑利用压力风扇、管道等辅助设备。

假设温室长50 m、宽11 m、高3.5 m，通过数值模拟给出臭氧的动态分布图，建立评价模型说明扩散方案的优劣。

（5）请分别给出在农业生产特别是水稻中杀虫剂使用策略、在温室中臭氧应用于病虫害防治的可行性分析报告，字数800-1000字。

论文题目：温室中的绿色生态臭氧病虫害防治姓名1：学号：专业：姓名1：学号：专业：姓名1：学号：专业：2010 年5月3日目录一.摘要 (3)二.问题的提出 (5)三.问题的分析 (5)四.建模过程 (6)1）问题一 (6)1.模型假设 (6)2.定义符号说明 (6)3.模型建立 (6)4.模型求解 (7)2）问题二 (9)1.基本假设 (9)2.定义符号说明 (10)3.模型建立 (10)4.模型求解 (11)3）问题三 (12)1.基本假设 (12)2.定义符号说明 (12)3.模型建立 (13)4.模型求解 (13)5.模型检验与分析 (14)6.效用评价函数 (15)7.方案 (16)4).问题四 (17)1.基本假设 (17)2.定义符号说明 (17)3.模型建立 (18)4.动态分布图 (19)5.评价方案 (19)五.模型的评价与改进 (20)六.参考文献 (21)一.摘要：“温室中的绿色生态臭氧病虫害防治”数学模型是通过臭氧来探讨如何有效地利用温室效应造福人类，减少其对人类的负面影响。

数学建模竞赛优秀论文集1. 引言1.1 概述：在当今飞速发展的科技时代，数学建模竞赛作为一种重要的科技比赛形式，逐渐受到广大学生的关注和参与。

通过数学建模竞赛，学生们能够运用已有的数学理论和方法，在实际问题中进行抽象、建模、求解和验证，培养了解决实际问题的能力和创新思维。

本文旨在整理并介绍数学建模竞赛中优秀的论文集，让更多人了解并受益于这些优秀的研究成果。

1.2 文章结构：本文共包括引言、正文、结论三个部分。

引言部分主要对文章内容进行概述，并介绍文章结构；正文部分将重点介绍数学建模竞赛的概述、优秀论文集意义以及论文选题与解题思路等方面内容；最后，结论部分将对全文进行总结反思，并展望数学建模竞赛发展前景。

1.3 目的：本文主要目的是通过整理数学建模竞赛优秀论文集，让更多的人了解到这些杰出作品所涉及到的研究领域和解决问题的方法，为相关领域的研究和实践提供借鉴和参考。

同时，本文也旨在激发更多学生对数学建模竞赛的兴趣，鼓励他们积极参与到这一学科竞赛中，提升自身数学建模能力和创新思维水平。

最终，希望通过这篇文章能够为数学建模竞赛的发展作出一定贡献，并推动相关研究领域的进步。

2. 正文：2.1 数学建模竞赛概述在数学建模竞赛中，参赛者们需要运用数学的知识和方法来解决实际问题。

这些问题往往来源于各个领域，如经济、生态、医学等，并涉及到数据处理、模型构建和结果验证等方面。

数学建模竞赛对于培养学生的综合素质和创新能力具有重要作用。

2.2 优秀论文集意义编撰一本数学建模竞赛优秀论文集是有一定价值的。

首先，这可以为广大参与数学建模的同学提供一个交流与借鉴的平台，让他们了解到其他队伍的解题思路和方法。

同时，这也为后来者们提供了宝贵的经验总结和参考资料。

其次，通过收集优秀论文，我们可以发现一些解决问题的通用方法或规律，并进一步推动相关领域研究的深入发展。

2.3 论文选题与解题思路在进行数学建模竞赛时，选题是至关重要的第一步。

大学生数学建模优秀论文(2)大学生数学建模优秀论文篇2浅谈数学建模与医学的关系众所周知，数学是一门以高度的抽象性、严谨性为特点的学科，但同时数学在其他各门学科也有广泛的应用性，而且随着大型计算机的飞速发展，数学也越来越多的渗透到各个领域中。

数学建模可以说是用数学方法解决实际问题的一个重要手段。

简单的说，用数学语言来描述实际问题，将它变成一个数学问题，然后用数学工具加以解决，这个过程就称为数学建模[1]。

人们通过对所要解决的问题建立数学模型，使许多实际问题得到了完满的解决。

如大型水坝的应力计算、中长期天气预报等。

建立在数学模型和计算机模拟基础上的CAD(Computer Aided Design)技术，以其快速、经济、方便等优势，大量地替代了传统工程设计中的现场实验、物理模拟等手段。

那么数学在医学领域有哪些应用呢? 现代的医学为什么要借助数学呢?本研究主要叙述这两个问题。

1 现代医学应用数学的必要性现代医学的大趋势是从定性研究走向定量研究，即要能够有效地探索医学科学领域中物质的量与量关系的规律性，推动医学科学突破狭隘经验的束缚，向着定量、精确、可计算、可预测、可控制的方向发展，并由此逐渐派生出生物医学工程学、数量遗传学、药代动力学、计量诊断学、计量治疗学、定量生理学等边缘学科，同时预防医学、基础医学和临床医学等传统学科也都在试图建立数学模式和运用数学理论方法来探索出其数量规律[2]。

而这些都要用到数学知识。

① 数学模型有助生物学家将某些变量隔离出来、预测未来实验的结果，或推论无法测量的种种关系，因为在实验中很难将研究的事物抽离出来单独观察。

尽管这些数学模型无法极其精确地模仿生命系统的运作机制，却有助于预测将来实验的结果。

② 可以利用数学分析实验数据资料。

当实验数据非常多时，传统的方法就不再适用了，只能转而使用数值计算的相关理论，以发现数据中存在的关联和规则。

特别地随着当前国际生命科学领域内最重要的基因组计划的发展，产生了前所未有的巨量生物医学数据。

09级数模试题1. 把四只脚的连线呈长方形的椅子往不平的地面上一放，通常只有三只脚着地，放不稳，然后稍微挪动几次，就可以使四只脚同时着地，放稳了。

试作合理的假设并建立数学模型说明这个现象。

（15分）解：对于此题，如果不用任何假设很难证明，结果很可能是否定的。

因此对这个问题我们假设 ：（1）地面为连续曲面（2）长方形桌的四条腿长度相同（3）相对于地面的弯曲程度而言，方桌的腿是足够长的（4）方桌的腿只要有一点接触地面就算着地。

那么，总可以让桌子的三条腿是同时接触到地面。

现在，我们来证明：如果上述假设条件成立，那么答案是肯定的。

以长方桌的中心为坐标原点作直角坐标系如图所示，方桌的四条腿分别在A 、B 、C 、D 处，A 、B,C 、D 的初始位置在与x 轴平行，再假设有一条在x 轴上的线ab,则ab 也与A 、B ，C 、D 平行。

当方桌绕中心0旋转时，对角线 ab 与x 轴的夹角记为θ。

容易看出，当四条腿尚未全部着地时，腿到地面的距离是不确定的。

为消除这一不确定性，令()f θ为A 、B 离地距离之和，()g θ为C 、D 离地距离之和，它们的值由θ唯一确定。

由假设（1），()f θ,()g θ均为θ的连续函数。

又由假设（3），三条腿总能同时着地， 故()f θ()g θ=0必成立（∀θ）。

不妨设(0)0f =,(0)0g >g （若(0)g 也为0，则初始时刻已四条腿着地，不必再旋转），于是问题归结为：已知()f θ,()g θ均为θ的连续函数，(0)0f =,(0)0g >且对任意θ有00()()0f g θθ=，求证存在某一0θ，使00()()0f g θθ=。

证明：当θ=π时，AB 与CD 互换位置，故()0f π>，()0g π=。

作()()()h f g θθθ=-，显然，()h θ也是θ的连续函数，(0)(0)(0)0h f g =-<而()()()0h f g πππ=->，由连续函数的取零值定理，存在0θ，00θπ<<，使得0()0h θ=，即00()()f g θθ=。

高教社杯全国大学生数学建模竞赛获奖论文（精品）2010高教社杯全国大学生数学建模竞赛编号专用页赛区评阅编号(由赛区组委会评阅前进行编号):赛区评阅记录(可供赛区评阅时使用):评阅人评分备注全国统一编号(由赛区组委会送交全国前编号):全国评阅编号(由全国组委会评阅前进行编号):关于2010年上海世博会影响力的评估——从历史文化交流方面进行讨论摘要本文从各国人民在历史文化方面的交流评估了2010年上海世博会的影响力。

根据题意以及互联网收集到的数据，建立了数学模型并定量估计了上海世博会的影响力，突出上海世博的主题“城市，让生活更美好”的基本理念。

首先，运用灰色聚类法对互联网收集到的数据进行灰类等级划分，再对数据进行无量纲化处理。

其次，建立各灰类白化函数，再对各组数据进行聚类权F运算，进而得出各因素的相应数据。

最后，通过白化函数得到的矩阵和聚类n权运算得到的函数，应用求聚类公式，求得各聚类对象的,,,fd*,LjjLLj，，,jL,1j各灰色聚类系数及结果。

然后应用层次分析法，推导出一种进行加权分析的方法，利用本方法对影响世博会的各个因素进行加权，得出了各个世博城市关于T，通过比较得到上海世博会影影响力的组合权重数据为(0.3634,0.3620,0.2743)响力均高于爱知、汉诺威世博会。

合适的评估体系是本课题的关键。

我们充分利用互联网收集到的数据进行分析及统计，并考虑到方案的可操作性。

通过组合权重数据，得到了三个世博城市关于影响力的权重。

由于此模型不受指数的影响，有很好的灵活性，使得我们可以根据实际情况灵活选取指数，减少模型的工作量，增加模型精度。

关键字:定量估计、层次分析法、灰色聚类法1一、问题重述2010年上海世博会是首次在中国举办的世界博览会。

从1851年伦敦的“万国工业博览会”开始，世博会正日益成为各国人民交流历史文化、展示科技成果、体现合作精神、展望未来发展等的重要舞台。

可以从我们感兴趣的某个侧面，建立数学模型，利用互联网数据，定量评估2010年上海世博会的影响力。

数学建模论文范文免费(必备14篇)试论数学建模【摘要】本文以“减肥问题的研究”为例，介绍了数学建模基本方法和步骤，希望它能对初次参加数学建模的同学有所帮助。

【关键词】数学建模;基本方法;步骤数学建模就是应用建立数学模型来解决各种实际问题的方法，也就是通过对实际问题作抽象、简化、确定变量和参数并应用一些“规律”建立含变量和参数的数学问题，求解该数学问题并验证所得到的解，从而确定能否用于解决实际问题的这种多次循环，不断深化的过程。

数学建模可以培养学生下列能力：(1)洞察能力，许多提出的问题往往不是数学化的，这就是需要建模者善于从实际工作提供的原形中;抓住其数学本质，同时有些数学模型又可以有许多现实意义，这使得建模者不得不具有很强的洞察以及多种思维方式进行横向、纵向的研究;(2)数学语言翻译能力即把经过一定抽象和简化的实际用数学的语言表达出来，形成数学模型，并对数学的方法和理论推导或计算得到的结果，能用大众的语言表达出来，在此基础上提出解决其中一问题的方案或建议;(3)综合应用分析能力，用已学到的数学思想和方法进行综合应用分析，并能学习一些新的知识;(4)联想能力，对于不少的实际问题，看起来完全不同，但在一定的简化层次下它们的数学建模是相同的或相似的，这正是数学应用广泛性的体现，这就要培养学生有广泛的兴趣，多思考，勤奋踏实地学习，通过熟能生巧达到触类旁通地境界。

因此，目前有越来越多的高等院校自己组织或参加全国乃至国际大学生数学建模竟赛。

然而，有部分学生特别是初次参加数学建模的学生对数学建模感到很茫然，本人多次承担数学建模指导老师，撰写该论文，希望对初次参加数学建模的同学有所帮助。

1.建立数学模型的一般步骤使问题理想化在众多因素中孤立出所研究的问题是科学研究的经典方法。

按照辩证唯物主义观点，世界上一切事物都是相互依赖、相互依存的，要精细地研究一个问题常常无从下手，就是因为思考相关问题太多所致。

因此，对初学者最好的方法就是使问题简单化、理想化，在特殊或极端情况下进入课题，然后加入相关因素，修正结果，使问题深化。

数学建模优秀论文(精选范文10篇)2021一、基于数学建模的空气质量预测研究本文以某城市为研究对象，通过数学建模方法对空气质量进行预测。

通过收集历史空气质量数据，构建空气质量预测模型。

运用机器学习算法对模型进行训练和优化，提高预测精度。

通过对预测结果的分析，为城市环境管理部门提供决策支持，有助于改善城市空气质量。

二、数学建模在物流优化中的应用本文针对某物流公司配送路线优化问题，运用数学建模方法进行求解。

建立物流配送模型，考虑配送成本、时间、距离等因素。

运用线性规划、遗传算法等优化算法对模型进行求解。

通过对求解结果的分析，为物流公司提供优化配送路线的建议，降低物流成本，提高配送效率。

三、基于数学建模的金融风险管理研究本文以某银行为研究对象，通过数学建模方法对金融风险进行管理。

构建金融风险预测模型，考虑市场风险、信用风险、操作风险等因素。

运用风险度量方法对模型进行评估。

通过对预测结果的分析，为银行提供风险控制策略，降低金融风险，提高银行稳健性。

四、数学建模在能源消耗优化中的应用本文针对某工厂能源消耗优化问题，运用数学建模方法进行求解。

建立能源消耗模型，考虑设备运行、生产计划等因素。

运用优化算法对模型进行求解。

通过对求解结果的分析，为工厂提供能源消耗优化策略，降低能源消耗，提高生产效益。

五、基于数学建模的交通流量预测研究本文以某城市交通流量为研究对象，通过数学建模方法进行预测。

收集历史交通流量数据，构建交通流量预测模型。

运用时间序列分析方法对模型进行训练和优化。

通过对预测结果的分析，为城市交通管理部门提供决策支持，有助于缓解城市交通拥堵。

数学建模优秀论文(精选范文10篇)2021六、数学建模在医疗资源优化配置中的应用本文以某地区医疗资源优化配置问题为研究对象，通过数学建模方法进行求解。

建立医疗资源需求模型，考虑人口分布、疾病类型等因素。

运用线性规划、遗传算法等优化算法对模型进行求解。

通过对求解结果的分析，为政府部门提供医疗资源优化配置策略，提高医疗服务质量。

精选五篇数学建模优秀论文一、基于深度学习的股票价格预测模型研究随着金融市场的发展，股票价格预测成为投资者关注的焦点。

本文提出了一种基于深度学习的股票价格预测模型，通过分析历史数据，预测未来股票价格走势。

实验结果表明，该模型具有较高的预测精度和鲁棒性，为投资者提供了一种有效的决策支持工具。

二、基于优化算法的智能交通信号控制策略研究随着城市化进程的加快，交通拥堵问题日益严重。

本文提出了一种基于优化算法的智能交通信号控制策略，通过优化信号灯的配时方案，实现交通流量的均衡分配，提高道路通行能力。

实验结果表明，该策略能够有效缓解交通拥堵，提高交通效率。

三、基于数据挖掘的电商平台用户行为分析电商平台在电子商务领域发挥着重要作用，用户行为分析对于电商平台的发展至关重要。

本文提出了一种基于数据挖掘的电商平台用户行为分析模型，通过分析用户购买行为、浏览行为等数据，挖掘用户偏好和需求。

实验结果表明，该模型能够有效识别用户行为特征，为电商平台提供个性化的推荐服务。

四、基于机器学习的疾病预测模型研究疾病预测对于公共卫生管理具有重要意义。

本文提出了一种基于机器学习的疾病预测模型，通过分析历史疾病数据，预测未来疾病的发生趋势。

实验结果表明，该模型具有较高的预测精度和可靠性，为疾病预防控制提供了一种有效的手段。

五、基于模糊数学的农业生产决策支持系统研究农业生产决策对于提高农业效益和农民收入具有重要意义。

本文提出了一种基于模糊数学的农业生产决策支持系统，通过分析农业环境、市场需求等因素，为农民提供合理的生产决策建议。

实验结果表明，该系统能够有效提高农业生产效益，促进农业可持续发展。

精选五篇数学建模优秀论文一、基于深度学习的股票价格预测模型研究随着金融市场的发展，股票价格预测成为投资者关注的焦点。

本文提出了一种基于深度学习的股票价格预测模型，通过分析历史数据，预测未来股票价格走势。

实验结果表明，该模型具有较高的预测精度和鲁棒性，为投资者提供了一种有效的决策支持工具。

数学建模获奖论文（优秀范文10篇）11000字数学建模竞赛从1992年始，到现如今已成为全国高校规模最大的基础性学科竞赛，也是世界上规模最大的数学建模竞赛。

本篇文章就为大家介绍一些数学建模获奖论文，供给大家欣赏和探讨。

数学建模获奖论文优秀范文10篇之第一篇：高中数学核心素养之数学建模能力培养的研究摘要：数学建模是一种比较重要的能力，教师在进行高中数学教学的过程中应该让学生们学习这种能力，这对于解决高中数学问题是比较有效的，而且对于学生们未来接受高等教育有更重要的意义。

教师在进行高中数学教学的过程中需要让学生们的能力得到锻炼，提升能力是教学的主要目的，学习知识是比较基础的教学目的，教师如果想让学生们的能力得到锻炼应该对教学方法进行更新，高中数学对于很多学生们来说都是比较困难的，所以教师应该不断更新教学方法，让学生们能理解教师的教学目的，而且找到适合自己的学习方法，这也是核心素养的基本内涵。

本文将对高中数学核心素养之数学建模能力培养进行研究。

关键词：高中数学; 核心素养; 数学建模; 能力培养; 应用研究;建模活动是一项比较有创造性的活动，学生们在学习的过程中一定要具备创新思维和自主学习能力，建模活动进行过程中可以让学生们独立，自觉运用数学理论知识去探索以及解决问题，构建模型解决实际问，教学活动中，让学生们的基础知识更加牢固、基本技能得到锻炼是最根本的目的。

学生们的运算能力以及逻辑思维能力也能在建模活动中得到锻炼，提升学生们的空间观念以及增强应用数学意识是延伸目的。

一、对数学建模的基本理解概述高中数学建模最简单的解释就是利用学生们学习过的理论知识来建立数学模型解决遇到的问题。

数学建模的基本过程就是对生活中或者课本中比较抽象问题解决的过程。

通过抽象可以建立刻画出一种较强的数学手段，通过运用数学思维也能观察分析各种事物的基本性质和特点。

学生们可以从复杂的问题中抽离出自己熟悉的模型，然后在利用好数学模型去解决实际问题基本就是事半功倍。