基于神经网络的马尾松毛虫精细化预报Matlab建模试验张国庆（安徽省潜山县林业局）1.数据来源马尾松毛虫发生量、发生期数据来源于潜山县监测数据，气象数据来源于国家气候中心。
2.数据预处理为了体现马尾松毛虫发生发展时间上的完整性，在数据处理时，将越冬代数据与上一年第二代数据合并，这样，就在时间上保持了一个马尾松毛虫世代的完整性，更便于建模和预测。
（1）气象数据处理根据《松毛虫综合管理》、《中国松毛虫》等学术资料以及近年来有关马尾松毛虫监测预报学术论文，初步选择与松毛虫发生量、发生期有一定相关性气象因子，包括卵期极低气温，卵期平均气温，卵期积温（日度），卵期降雨量，第1、2龄极低气温，第1、2龄平均气温，第1、2龄积温（日度），第12龄降雨量，幼虫期极低气温，幼虫期平均气温，幼虫期积温（日度），幼虫期降雨量，世代极低气温，世代平均气温，世代积温（日度），世代降雨量共16个变量。
将来自于国家气候中心的气象原始数据，按年度分世代转换成上述16个变量数据系列。
（2）发生量数据处理为了在建模时分析发生强度，在对潜山县1983～2014年原始监测数据预处理时，按照“轻”、“中”、“重”3个强度等级，分类按世代逐年汇总。
（3）发生期数据处理首先对潜山县1983～2014年原始发生期监测数据按世代逐年汇总，然后日期数据转换成日历天，使之数量化，以便于建模分析。
3.因子变量选择通过相关性分析和建模试验比较，第一代发生量因子变量选择第1、2龄极低气温，卵期极低气温，上一代防治效果，上一代防治面积；第二代发生量因子变量选择第1、2龄极低气温，卵期极低气温，上一代防治效果，上一代防治面积，第1、2龄降雨量，卵期降雨量；第一代幼虫高峰期因子变量选择第1、2龄平均气温，第1、2龄积温（日度），第1、2龄极低气温，卵期极低气温；第二代幼虫高峰期因子变量选择成虫始见期，卵期平均气温，卵期积温（日度），第1、2龄极低气温。
将第一代发生量变量命名为s1y，因变量命名为s1x；第二代发生量变量命名为s2y，因变量命名为s2x；第一代幼虫高峰期变量命名为t1y，因变量命名为t1x；第二代幼虫高峰期变量命名为t2y，因变量命名为t2x。
4.第一代发生量建模试验4.1程序代码程序代码（Simple Script）为：% Solve an Input-Output Fitting problem with a Neural Network% Script generated by Neural Fitting app% Created Wed Oct 28 19:28:48 CST 2015%% This script assumes these variables are defined:%% s1x - input data.% s1y - target data.x = s1x';t = s1y';% Choose a Training Function% For a list of all training functions type: help nntrain% 'trainlm' is usually fastest.% 'trainbr' takes longer but may be better for challenging problems.% 'trainscg' uses less memory. NFTOOL falls back to this in low memory situations.trainFcn = 'trainlm'; % Levenberg-Marquardt% Create a Fitting NetworkhiddenLayerSize = 10;net = fitnet(hiddenLayerSize,trainFcn);% Setup Division of Data for Training, Validation, Testingnet.divideParam.trainRatio = 90/100;net.divideParam.valRatio = 5/100;net.divideParam.testRatio = 5/100;% Train the Network[net,tr] = train(net,x,t);% Test the Networky = net(x);e = gsubtract(t,y);performance = perform(net,t,y)% View the Networkview(net)% Plots% Uncomment these lines to enable various plots.%figure, plotperform(tr)%figure, plottrainstate(tr)%figure, plotfit(net,x,t)%figure, plotregression(t,y)%figure, ploterrhist(e)程序代码（Advanced Script）为：% Solve an Input-Output Fitting problem with a Neural Network% Script generated by Neural Fitting app% Created Wed Oct 28 19:29:03 CST 2015%% This script assumes these variables are defined:%% s1x - input data.% s1y - target data.x = s1x';t = s1y';% Choose a Training Function% For a list of all training functions type: help nntrain% 'trainlm' is usually fastest.% 'trainbr' takes longer but may be better for challenging problems.% 'trainscg' uses less memory. NFTOOL falls back to this in low memory situations.trainFcn = 'trainlm'; % Levenberg-Marquardt% Create a Fitting NetworkhiddenLayerSize = 10;net = fitnet(hiddenLayerSize,trainFcn);% Choose Input and Output Pre/Post-Processing Functions% For a list of all processing functions type: help nnprocessnet.input.processFcns = {'removeconstantrows','mapminmax'};net.output.processFcns = {'removeconstantrows','mapminmax'};% Setup Division of Data for Training, Validation, Testing% For a list of all data division functions type: help nndividenet.divideFcn = 'dividerand'; % Divide data randomlynet.divideMode = 'sample'; % Divide up every samplenet.divideParam.trainRatio = 90/100;net.divideParam.valRatio = 5/100;net.divideParam.testRatio = 5/100;% Choose a Performance Function% For a list of all performance functions type: help nnperformance net.performFcn = 'mse'; % Mean squared error% Choose Plot Functions% For a list of all plot functions type: help nnplotnet.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...'plotregression', 'plotfit'};% Train the Network[net,tr] = train(net,x,t);% Test the Networky = net(x);e = gsubtract(t,y);performance = perform(net,t,y)% Recalculate Training, Validation and Test PerformancetrainTargets = t .* tr.trainMask{1};valTargets = t .* tr.valMask{1};testTargets = t .* tr.testMask{1};trainPerformance = perform(net,trainTargets,y)valPerformance = perform(net,valTargets,y)testPerformance = perform(net,testTargets,y)% View the Networkview(net)% Plots% Uncomment these lines to enable various plots.%figure, plotperform(tr)%figure, plottrainstate(tr)%figure, plotfit(net,x,t)%figure, plotregression(t,y)%figure, ploterrhist(e)% Deployment% Change the (false) values to (true) to enable the following code blocks. if (false)% Generate MATLAB function for neural network for application deployment % in MATLAB scripts or with MATLAB Compiler and Builder tools, or simply % to examine the calculations your trained neural network performs. genFunction(net,'myNeuralNetworkFunction');y = myNeuralNetworkFunction(x);endif (false)% Generate a matrix-only MATLAB function for neural network code% generation with MATLAB Coder tools.genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');y = myNeuralNetworkFunction(x);endif (false)% Generate a Simulink diagram for simulation or deployment with. % Simulink Coder tools.gensim(net);end4.2网络训练过程网络训练为：图1 第一代发生量网络训练过程4.3训练结果训练结果为：图2 第一代发生量网络训练结果训练样本、验证样本、测试样本的R值分别为0.875337、-1和1。
