AHP-Based Equipment Selection Model for Construction
Projects
Aviad Shapira,M.ASCE,1and Marat Goldenberg2
Abstract:Selection of equipment for construction projects,a key factor in the success of the project,is a complex process.Current models offered by the literature fail to provide adequate solutions for two major issues:the systematic evaluation of soft factors,and the weighting of soft bene?ts in comparison with costs.This paper presents a selection model based on analytic hierarchy process?AHP?,a multiattribute decision analysis method,with a view to providing solutions for these two issues.The model has the capacity to handle a great number of different criteria in a way that truly re?ects the complex reality,to incorporate the context and unique conditions of the project,and to allow for manifestation of user experience and subjective perception.The model was implemented in an in-house developed system that was improved and validated through testing by senior professionals.The main academic contribution of the study is in the modi?cation of AHP to correspond with the nature of equipment selection and in its utilization as an effective means for the formalization of knowledge possessed by competent,experienced practitioners.On the practical side,the proposed model offers an ef?cient,convenient tool that forces the users into orderly,methodical thinking,guides them in making logical,consistent decisions,and provides a facility for all necessary computations.
DOI:10.1061/?ASCE?0733-9364?2005?131:12?1263?
CE Database subject headings:Construction equipment;Cost control;Construction management;Decision making.
Introduction
Selecting the right equipment has always been a key factor in the success of any construction project;this is even more so in to-day’s complex,highly industrialized projects?O’Brien et al. 1996;Schaufelberger1999;Nunnally2000;Harris and McCaffer 2001;Peurifoy et al.2006?.It is no wonder then that the subject has generated quite a volume of academic studies that attempt to improve both the process and its?nal product.A study of equip-ment selection models?Shapira and Goldenberg2005?has shown that,although they present novel concepts and introduce ad-vanced tools,the majority of the state-of-the-art models?e.g., Hanna and Lotfallah1999;Zhang et al.1999;Al-Hussein et al. 2000,2001;Tam et al.2001;Sawhney and Mund2001?:?1?limit themselves to dealing with only part of the problem,without systematic consideration of the entire site plan as a whole;?2?consider mainly“hard”factors?such as costs and technical con-straints of the site and project?,while failing to provide a stage for“soft”?i.e.,qualitative,intangible,informal?considerations;?3?aim at providing a general,“universal”solution,which re-quires the models to simplify the problem and,consequently,
to lose their ability to truly and fully re?ect the project’s com-
plexity and its unique contextual conditions;and/or?4?fail to
provide for the incorporation of the subjective,intuitive judgment
of the decision maker.
These conclusions were further supported by?ndings of a?eld
survey conducted among successful project managers with expe-
rience in the construction of sizable,complex projects?Shapira
and Goldenberg2005?.By relying heavily on their vast experi-
ence and professional skills,these practitioners managed to make
what appeared to be“right”decisions in selecting equipment for
their projects,despite two major obstacles,as attested by them:?1?the lack of a method for the systematic evaluation of soft factors;and?2?the lack of a structured process for the rational
integration of cost estimates on the one hand,and soft consider-
ations on the other hand.
Thus the objective of this study was to develop an equipment
selection model that will both overcome the limitations of exist-
ing models,as offered by the current literature,and provide solu-
tions for the prevalent issues,as identi?ed in current practices.
The characterization of the equipment selection process as an
essentially multifaceted problem involving numerous,variegated
considerations,often with complex trade-offs among them,im-
plied that a suitable solution method might be found among the
family of multiattribute-decision-analysis?MADA?methods ?Norris and Marshall1995?.Further analysis and pro?ling of the selection problem and the identi?cation of the solution method’s desirable capabilities,triggered the consideration of analytic hier-archy process?AHP??Saaty1980?as a possible basis for the equipment selection model envisaged.
This paper?rst presents the essence of AHP,its suitability for
the equipment selection problem,and its solution mechanism.
1Associate Professor,Dept.of Civil and Environmental Engineering, Technion–Israel Institute of Technology,Haifa32000,Israel.
2Project Of?cer,Civil Engineering Division,Israel Air Force; formerly,Graduate Student,Dept.of Civil and Environmental Engineering,Technion–Israel Institute of Technology,Haifa32000, Israel.
Note.Discussion open until May1,2006.Separate discussions must be submitted for individual papers.To extend the closing date by one month,a written request must be?led with the ASCE Managing Editor. The manuscript for this paper was submitted for review and possible publication on November11,2003;approved on October28,2004.This paper is part of the Journal of Construction Engineering and Manage-ment,V ol.131,No.12,December1,2005.?ASCE,ISSN0733-9364/ 2005/12-1263–1273/$25.00.
The paper then introduces the proposed selection model,with a focus on two modules,which were mentioned as worthy of fur-ther research in the conclusion of the complete report on the subject?Shapira and Goldenberg2005?.These two modules,the evaluation of soft factors and the overall evaluation of costs ver-sus soft factors,are regarded by the writers to be the main con-tribution of the present paper.The paper ends with the presenta-tion of the model’s implementation and testing. Fundamentals and Principles of Analytic Hierarchy Process
AHP Approach
Saaty?1980??rst introduced AHP as a new approach to dealing with complex economic,technological,and sociopolitical prob-lems,which often involve a great deal of uncertainty.A typical MADA method,AHP was developed to assist in the making of decisions that are characterized by a great number of interrelated and often contending factors.To make such decisions,the relative importance of the factors involved must be properly assessed in order to enable trade-offs among them.The main feature of AHP is its inherent capability of systematically dealing with a vast number of intangible and nonquanti?able attributes,as well as with tangible and objective factors.AHP allows for the incorpo-ration into the decision-making process of subjective judgments and user intuition by producing a common formal and numeric basis for solution.
Over the years,AHP has been implemented successfully in various areas.A literature survey found only a few AHP applica-tions in the area of construction,as follows:advanced construc-tion technologies/processes/materials evaluation?Skibniewski and Chao1992;Hastak1998;Hastak and Halpin2000?,contrac-tor selection?Fong and Choi2000?,procurement selection?Che-ung et al.2001a?,alternative dispute resolution?Cheung et al. 2004?,and project management?Al-Harbi2001?.No AHP appli-cation,however,was found that deals with the selection of con-struction equipment or similar issues.
AHP Basics
According to Saaty,AHP is based on a two-stage process:?rst,“decomposing the complexity”by identifying the?“small”?fac-tors that make up the?“big”?problem and mapping their mutual relationships,and then“synthesizing the relations”by determin-ing the relative weights of the factors and aggregating their cu-mulative effect vis-à-vis a single decision criterion.
Five basic AHP elements were identi?ed and are presented brie?y below.They are elaborated on later,as required,in relation to the proposed model.
Hierarchy Construction
To“decompose the complexity,”decision factors are organized in a hierarchy-type structure.The primary goal of the problem?e.g., successful selection of equipment?occupies the highest level of the structure,followed by“sets of attributes”that are organized in several more hierarchy levels.A typical second-level attribute set includes all of the secondary goals that together contribute to achieving the primary goal?e.g.,economy,schedule,ef?ciency, and safety?.These,in turn,are directly affected by all of the attributes in the set located one level lower?e.g.,safety may be affected by winds,power lines,crane overlapping,night work,etc.?,and so on,as dictated by the nature of the problem.Thus a set is located either under another attribute that is one level higher,or under the primary goal of the problem.Attributes that have no other attributes under them in the hierarchy structure are termed“leaf attributes.”All in all,this hierarchy structure ex-presses the interrelationships between the various decision fac-tors.At the lowest AHP hierarchy level is a set of feasible alter-natives that are to be evaluated.Alternatives must be connected to all of the leaf attributes potentially affecting their evaluation. Pairwise Comparisons
Once interrelationships between attributes?i.e.,decision factors?are mapped by the hierarchy,relative weights of the attributes are determined by comparing them in pairs,separately for each set in the hierarchy.The results for each set are recorded in a separate “decision matrix.”When comparing two attributes,the following must be determined:?1?which attribute is more important or has greater in?uence on the attribute one level higher in the hierarchy ?e.g.,what affects safety more—winds or power lines;what af-fects safety more—winds or crane overlapping;and so on?;and ?2?what is the intensity of that importance?e.g.,weak,strong,
absolute?.Verbal intensity assessments are translated into num-bers?thus,in fact,converting qualitative evaluations into quanti-tative ones?according to a given scale?Saaty1980?.It should be noted that the pairwise-comparison method is perhaps the corner-stone of the entire AHP philosophy,as it allows the user to sys-tematically determine the intensities of interrelationships of a great—practically unlimited—number of decision factors. Relative-Weight Calculation
One of Saaty’s core theorems states that the eigenvector of the decision matrix established in the previous phase?i.e.,the out-come of the pairwise comparison process?is the priority vector of the attributes compared,which represents their relative weights with regard to the attribute located one level higher in the hierar-chy.AHP mathematical foundations are relatively simple;the reader is referred to Saaty?1980?for a more detailed presentation of the method and its calculation techniques.Several approxima-tion methods can be used to calculate the eigenvector,w?,of the decision matrix,of which the average of normalized columns ?ANC?method is the most accurate?Saaty1980?.An ANC cal-culation of w i,the relative weight of the attribute in row i?which is an element of the eigenvector w??,for a reciprocal n?n matrix, is as follows:
w i=
1
n
·?j=1
n
a ij
?k=1n a kj?1?
where a ij=element located in row i and column j of the decision matrix.
The screen capture in Fig.3,presented later to illustrate the implementation of the proposed model,serves also as an example for the above calculation procedure.
Aggregation of Relative Weights
Once relative weights are calculated for each set of attributes at every level of the hierarchy and respective local priority vectors are produced,the overall score of each alternative,representing the preference of one alternative over another,can now be ob-tained.Aggregation is achieved by multiplying local priority vec-tors of each set of attributes by the relative weights of the respec-tive attributes immediately above them,starting at the lowest level and ending at the primary goal level.The new vector ob-
tained is no longer local but rather inclusive for the entire hierar-
chy.The sum total of all such computations for each leaf attribute
in the hierarchy is1.00.The overall score of the alternatives is
obtained by multiplying the local priority vector of the alterna-
tives with respect to each leaf attribute by the inclusive priority
vector of that leaf attribute,and summing the products.This score
enables a comparison between the alternatives on a weighted
basis,representing the entire collection of decision factors in the
hierarchy.
Consistency Ratio
The?fth element of AHP,the“Consistency Ratio”?CR?measure,
is a tool for controlling the consistency of pairwise comparisons.
Since one of the advantages of AHP is its ability to allow subjec-
tive judgment,and with intuition playing an important role in the
selection of the best alternative,absolute consistency in the pair-
wise comparison procedure should not be expected.“Absolute
consistency”means,for example,that if x is more important than
y by a factor of2,and y is more important than z by a factor of3, then x should be more important than z by a factor of6.The CR,
introduced by Saaty?1980?and computed using a formula he
developed,enables one to control the extent of inconsistency to a
maximum desirable level,for each decision matrix and for the
entire hierarchy.Based on numerous empirical studies,Saaty ?1980?stated that to be acceptable?i.e.,for tolerable inconsis-tency?,the CR must be less than or equal to0.10?irrespective of
the nature of the problem?;if this condition is not ful?lled,a
revision of the comparisons is recommended.It must be stressed,
however,that an acceptable CR does not guarantee a“good”?nal
selection outcome.Rather,it ensures only that no intolerable con-
?icts exist in the comparisons made,and that the decision is logi-cally sound and not a result of random prioritization.
Limitations of AHP
The introduction of AHP and the increase in its popularity encour-aged intensive studies and some criticism of that innovative method.The main issues raised included:?1?the likelihood of the occurrence of the“rank reversal”phenomenon,in which an alter-native determined as best is not chosen after all when a certain other alternative is removed from the set of alternatives?Belton and Gear1983;Dyer1990?;?2?the imposed inconsistency due to the restriction of pairwise comparisons to a1-to-9scale and to the problematic correspondence between the verbal and the numeric scales?Belton1986;Belton and Goodwin1996?;and?3?the variation in verbal expressions from one person to another,as well as their dependence on the type of elements involved in the comparison?P?yh?nen et al.1995?.
Saaty?1990?;Saaty and Vargas?1991?;and Pérez?1995?re-sponded to the criticism,showing that AHP principles and scale have a solid theoretical and practical basis.Saaty and Vargas ?1991?demonstrated how,in certain applications,“rank reversal”is entirely normal and even desirable,while in other cases,abso-
lute measurement of alternatives can be used instead of relative measurement to avoid rank reversal?as in the proposed model?. Belton and Goodwin?1996?responded to their own critique by concluding that ensuring the user’s full understanding of the ques-tions posed during the pairwise comparisons will guarantee the proper use of AHP.
Equipment Selection Model
The proposed AHP-based equipment selection model does not constitute merely a technical solution for an isolated problem,but rather represents a comprehensive concept of the entire selection process.As outlined in Fig.1,the model comprises three central modules:?1?cost evaluation of alternatives;?2?bene?t evaluation of alternatives;and?3?total evaluation of alternatives.Of these three modules,the current paper focuses on the latter two,and thus responds to two major de?ciencies in current equipment se-lection practices identi?ed earlier?Shapira and Goldenberg2005?, namely the evaluation of alternatives with respect to soft factors, and the integration of both costs and soft factors to produce a single desirable solution.Thus the?rst module,cost evaluation—indispensable but inherently technical,covered extensively by textbooks?Illingworth1993;Nunnally2000;Harris and McCaf-fer2001;Peurifoy et al.2006?,and admittedly exercised properly in current practices—is addressed here only to the extent of its implications for the other two modules and its integration within the entire process.
Information Gathering and Generation of Alternatives The selection process starts with the preliminary,yet critical phase of information gathering and generation of feasible alterna-tives?i.e.,those satisfying all threshold requirements?.Although the current study does not treat this phase speci?cally,one aspect of it must be stressed,as it might affect the ensuing phases as well as the outcome of the entire selection process?Goldenberg2002?: Since the ultimate goal is the best overall production system pos-sible,the scope of the process should extend beyond the mere generation of different equipment alternatives,and should include also an investigation of the possible revision of construction methods?Nunnally2000?.Moving from cast-in-place concrete to precast elements,for example,might have a major effect on cran-ing and concrete placing
requirements.
Fig.1.Proposed equipment selection model
AHP-Based Bene?t Evaluation
As stated earlier,the Bene?t Evaluation Module,essentially a modi?cation of the AHP method to suit the problem of equipment selection,is one of the main novelties offered by the current paper.It should be noted,however,that this module may be skipped,at the discretion of the selection team,when a feasible alternative exists that is considerably lower in cost compared to the other alternatives?as also shown in Fig.1?.
Hierarchy Construction
Hierarchy construction,the?rst phase of the AHP-based bene?t evaluation,is also the?rst step that involves adjustment of the solution to the context of the problem,since only decision factors relevant to the project under discussion are determined as at-tributes and grouped into sets at the various hierarchy levels.For example,“Working night shifts”will be an attribute only if night work is indeed planned?in at least one alternative?.Hierarchy construction is a typical“trial and error”process.The hierarchy exempli?ed in Fig.2is a likely outcome of the process,provided all factors are indeed relevant to the examined project.
The following should be kept in mind when constructing the hierarchy.
?There is no one“correct”hierarchy.Hierarchy construction is subjective and greatly affected by common practices and tra-ditions of the speci?c construction company,as well as by the experience and preferences of the users.At the same time, hierarchies built in different companies?or by different users in the same company?are likely to differ from each other more at lower than at higher levels of the hierarchy.?Similarly,context plays a greater role the lower we go in the hierarchy.In other words,the extent of adjustment to the spe-ci?c conditions of the project under discussion will not be felt at all at the primary-goal level?i.e.,“Successful selection of equipment”?,it might be greater,yet relatively low,at the secondary-goals level?see Fig.2,Level2?,and will be most discernable at the decision-factors level?see Fig.2,Level3?.?All decision factors in any given set should be clearly related
to the factor immediately above them,which constitutes the common basis for the pairwise comparisons.
?When referring to any attribute in the hierarchy,its name must be closely associated with its exact meaning and perceived as such by the user.Confusing names might lead to errone-ous location of attributes and to inconsistencies in pairwise comparisons.
?To avoid duplication,a hierarchy must not include cost factors ?including soft factors that directly affect costs?,as these are dealt with in a separate module,and their cumulative in?uence is also taken into account in the course of the total evaluation.
Moreover,certain typical soft factors are likely to have been considered previously in the generation and formulation of alternatives,and hence should not be repeated in the hierarchy.
Examples include company policy towards owning of equip-ment versus rental,company project specialization and project forecast,and commercial considerations in cash-?ow produc-ing facilities.
?The inclusion of factors with regard to which all alternatives are identical should be avoided,even if such factors are essen-tially relevant,so as not to needlessly burden the solution pro-cess.For example,“Noise levels”is a relevant decision factor for projects located in urban areas.If,however,all alternatives are indifferent to noise?e.g.,only electric powered cranes are used,or no work is planned for irregular hours?,then there is no need to include“Noise levels”in the hierarchy.
?A decision factor is not limited to belonging to only one set, and can appear in two or more sets,provided it has a different type of in?uence in each set.For example,“Winds”may affect both safety and progress delays,and,if relevant,can appear in two different sets?see also Fig.2?.
?Proceeding on to the next phase and starting the performance of pairwise comparisons does not necessarily mean that con-struction of the hierarchy has been?nalized.If dif?culties with comparisons are encountered,they may serve as indication that the hierarchy should be
revised. Fig.2.Hierarchy for equipment selection
Pairwise Comparisons
At the next phase of the solution,pairs of factors are compared in order to systematically determine the relative in?uence of the factors on the attribute positioned one level higher in the hierar-chy.The comparisons depend on the constraints of the speci?c project under discussion,but at this stage,the alternatives them-selves do not yet play a https://www.doczj.com/doc/155479713.html,parisons are performed sepa-rately for each set in the hierarchy by responding to a leading question.A typical question for Level2of the hierarchy?see Fig. 2?would be,“What might have a greater in?uence on the suc-cessful selection of equipment?for the particular project exam-ined?:work safety or progress delays,and to what extent?”Simi-larly,a typical Level3question would be,“What might have a greater in?uence on progress delays?for the particular project examined?:heavy traf?c or labor availability,and to what ex-tent?”In this way,all factors of each set are compared with each other in pairs,resulting in a total of n·?n?1?/2such comparisons for a set containing n elements.Note that unlike the previous phase,in which the hierarchy exempli?ed in Fig.2may indeed serve as a basis for similar cases,the results of the pairwise com-parisons are completely project-dependent and user-dependent; hence no general scheme can be outlined.The main distinction between different cases is the variation in the intensities of the relative in?uences of the factors,as assessed verbally and then translated into numbers using the given AHP scale?Saaty1980?. Pair comparisons constitute,in effect,the input of various project data through which the users can express their speci?c prefer-ences regarding the given project.For a certain project,progress and schedule may be viewed as dominant,while for another, safety considerations may govern.Moreover,it is quite possible that for the very same project two different users will have dif-ferent preferences due to different experience,perspectives,and attitudes.
The decision matrix presented in Fig.3is an example outcome of pairwise comparisons conducted for“Managerial convenience”?see Fig.2?.As evident from the matrix,three of the four factors were assessed as being equal?total of3comparisons for n=3?, hence the value1?corresponding to“Two attributes contribute equally to the objective”?occurs three times in the matrix?see shaded cells?.The importance of“Previous experience with equipment”relative to“Managerial convenience”was assessed as inferior to that of the other three factors:by a value of5compared to“Dependence on outsourcing”and“Pieces of equipment to manage”?i.e.,each is“favored strongly”over“Previous experi-ence”?,and by a factor of3compared to“Working night shifts”?i.e.,the latter is“favored slightly”?.The values in the upper row of the matrix are the reciprocal values of5,5,and3,
respectively. Fig.3.Model implementation:screen capture of example pairwise comparison
The comparison and its outcome are indifferent to the order in which the factors are placed in the hierarchy and then compared to each other.
The following guidelines should be observed at this phase.?In order to assure accurate results ?i.e.,results that truly express the perception of the user ?,it is advisable,in each comparison,to actually repeat the leading question.When test-ing the model,it was found that users tend to memorize the leading question and to forsake it after several comparisons.This tended to result in CRs that were greater than 0.10?see “Consistency Ratio”above ?.When comparisons were repeated with greater adherence to the leading question,CR results improved.
?Users should refrain from dealing with more than one pair of factors at a time.Although the tendency to handle all factors in a set at once,and to rate them relative to each other instead of in pairs may seem natural to some users,this practice violates the AHP rationale and procedure and might impair the results.?For each set,the leading question must refer only to the one attribute located immediately above the said https://www.doczj.com/doc/155479713.html,ers should pay particular attention when comparing factors that belong to more than one set in the hierarchy ?and pose a different ques-tion for each set ?.
?As concluded from the testing of the model ?see “Model Implementation and Testing”?,users will acquaint themselves with the solution method faster,gain con?dence in running the model faster,and use the model more ef?ciently if pairwise comparisons start with a Level-2set that is “closer to the us-er’s heart”or more comprehensible than other sets.Similarly,the ?rst comparisons within the set ?i.e.,at Level 3?should be conducted on factors easily grasped by the user.
?Whenever the CR is greater than 0.10,comparisons should be repeated and revised.If the revised comparison does not achieve an acceptable CR ?i.e.,?0.10?,the hierarchy should be reexamined.This situation is likely to coincide with confu-sion or dif?culty experienced by the users in their responding to the leading question.A likely solution is to correct the lo-cation of a factor in the hierarchy ?by moving it from one set to another ?,rephrase the factor,create a new set of factors,or even reconstruct the entire hierarchy.
Similar to hierarchy construction,pairwise comparison is a user-induced solution phase.It is accompanied by two concurrent steps,performed without user intervention,in which the results of the comparisons are processed and their consistency checked.These steps are the eigenvector computations,which,at a later phase,serve in the calculation of the ?nal score of each alterna-tive,and CR examination,as mentioned above ?see “AHP Basics”?.The results of the eigenvector computations will be as-sembled as shown in Table 1,columns 2and 4.The rest of Table
Table 1.Template for Bene?ts Evaluation of Alternatives
Level 2Level 3Scale for absolute scores Absolute scores Aggregation of relative weights
Attribute Relative weight Attribute
Relative weight Score Relative weight Alt.1a
…Alt.m a
Alt.1b
…Alt.m c 1234567…89…10F 1
RW ?F 1?
F 1.1
RW ?F 1.1?
S 1RW ?S 1?AS 1?F 1.1,A 1?.
AS 1?F 1.1,A m ?AW 1?F 1.1,S 1?.
AW m ?F 1.1,S 1?S 2RW ?S 2?AS 2?F 1.1,A 1?AS 2?F 1.1,A m ?AW 1?F 1.1,S 2?AW m ?F 1.1,S 2?S 3RW ?S 3?AS 3?F 1.1,A 1?
AS 3?F 1.1,A m ?
AW 1?F 1.1,S 3?
AW m ?F 1.1,S 3?
..S 1RW ?S 1?.....
...S 2RW ?S 2?......S 3RW ?S 3?.
.
.
.
F 1.p RW ?F 1.p ?
S 1RW ?S 1?AS 1?F 1.p ,A 1?.AS 1?F 1.p ,A m ?AW 1?F 1.p ,S 1?.
AW m ?F 1.p ,S 1?S 2RW ?S 2?AS 2?F 1.p ,A 1?AS 2?F 1.p ,A m ?AW 1?F 1.p ,S 2?AW m ?F 1.p ,S 2?S 3RW ?S 3?AS 3?F 1.p ,A 1?
AS 3?F 1.p ,A m ?
AW 1?F 1.p ,S 3?
AW m ?F 1.p ,S 3?
...............................
..
.
..
F n RW ?F n ?
F n .1RW ?F n .1?S 1RW ?S 1?AS 1?F n .1,A 1?.AS 1?F n .1,A m ?AW 1?F n .1,S 1?.
AW m ?F n .1,S 1?S 2RW ?S 2?AS 2?F n .1,A 1?AS 2?F n .1,A m ?AW 1?F n .1,S 2?AW m ?F n .1,S 2?S 3RW ?S 3?AS 3?F n .1,A 1?
AS 3?F n .1,A m ?
AW 1?F n .1,S 3?
AW m ?F n .1,S 3?
..S 1RW ?S 1?.....
...S 2RW ?S 2?......S 3RW ?S 3?.
.
.
.
F n .q RW ?F n .q ?
S 1RW ?S 1?AS 1?F n .q ,A 1?.AS 1?F n .q ,A m ?AW 1?F n .q ,S 1?.
AW m ?F n .q ,S 1?S 2RW ?S 2?AS 2?F n .q ,A 1?AS 2?F n .q ,A m ?AW 1?F n .q ,S 2?AW m ?F n .q ,S 2?S 3
RW ?S 3?
AS 3?F n .q ,A 1?
AS 3?F n .q ,A m ?
AW 1?F n .q ,S 3?
AW m ?F n .q ,S 3?
Sum of aggregated weights:
?AW 1?F i .j ,S k ?…?AW m ?F i .j ,S k ?
Normalized bene?t score of alternatives:
?AW 1??i =1m
?AW i …?AW m ?
?i =1m ?AW i
a
The values in each cell in columns 7and 8are either 1.0or 0.0.Of the three optional scores for each attribute at Level 3—S 1,S 2,S 3—only one will be assigned the value 1.0,and the other two will be assigned the value 0.0.b
Column 9=2?4?6?7.c
Column 10=2?4?6?8.
1,which serves as a template for the entire AHP-based bene?t
evaluation process,pertains to the following steps.
Scoring of Alternatives
User input is solicited again to determine the extent to which each
equipment option satis?es each soft factor.Evaluation of alterna-
tives can be performed either in accordance with the original AHP
method?Saaty1980?,that is by comparing them in pairs in rela-
tion to all leaf attributes?e.g.,all Level3decision factors in the
example hierarchy in Fig.2?,or by assigning each alternative an
absolute score?Saaty and Vargas1991?.Absolute scoring—
termed also absolute scale measurement/assessment—is more ex-
peditious than the former method:it is easier to rank the alterna-
tives and,overall,fewer rankings are required?by a factor of ?m?1?/2for m alternatives?.Absolute scale measurement can be used instead of relative scale measurement in cases in which each
alternative is measured individually,on its own merit,with no
dependence on the measurement of the other alternatives.Since
the assessment of feasible equipment options falls within this cat-
egory,and in view of the aforementioned advantage of absolute
scoring in overcoming the rank reversal phenomenon,this mea-
surement mode was favored in the current case.Hastak?1998?
and Hastak and Halpin?2000?,who examined this issue with
regard to AHP-based evaluation of advanced construction meth-
ods and materials,reached similar conclusions and also adopted
absolute scale measurement in their work.
Alternatives are each,individually,assigned a score for each
leaf attribute,in response to the leading question?e.g.,“How
would Alternative B fare with regard to‘Coverage of staging
areas by cranes?’”?.The current model uses a three-level scale ?“good,”“fair,”and“bad”?,as was also found satisfactory by Hastak and Halpin?2000?.Score preferences are then de?ned numerically,using the AHP pairwise comparison technique.This can be done either separately for each leaf attribute?Hastak1998; Hastak and Halpin2000?,or collectively,for all attributes to-gether.The two methods were tested and compared during the development of the current model,and the?nal results?i.e., equipment option scores?were found to be almost identical.Nev-ertheless,it was found that the latter method simpli?es the process considerably by avoiding the laborious repetition of pair-wise comparisons of same-name rankings in relation to each and every one of the leaf attributes?e.g.,18,for the case presented in Fig.2?.This simpli?cation of the process constitutes a major ad-vantage and this method was therefore adopted for the current model.Indeed,if higher accuracy is desired,the same method,yet with a greater number of rankings?e.g.,a?ve-level scale?,can be used.Table1,column6lists the eigenvectors—identical for all attributes—of the three-level scoring scale used in the current model.Columns7and8assemble the scoring results for each Level3attribute:1.0for the one applicable score,0.0for the two other scores.Scores are denoted by the number1.0or0.0to enable the arithmetic required in the subsequent aggregation phase.
Aggregation of Relative Weights
The last phase of the AHP-based evaluation of equipment op-
tions vis-à-vis soft factors is,once again,performed automati-
cally,without user intervention.Results obtained from the two
previous phases—the pairwise comparisons and the scoring of the
alternatives—are aggregated to produce a quantitative measure of
the bene?ts offered by each equipment option considered.The
procedure is performed in a sequence?see“AHP Basics”above?.
It consists of the multiplication,for each alternative,of its score with regard to each selection criterion by the relative weight of that criterion,followed by the summing of the products.The total AHP score obtained for each alternative represents its relative value with respect to all selection criteria in the hierarchy.Col-umns9and10of Table1assemble the relative weight of each factor for each alternative?for Alt.1:column9?column 2?column4?column6?column7;for Alt.m:column 10?column2?column4?column6?column8?.The total AHP-based bene?t score for each alternative is listed at the bottom of the table.
Total Evaluation
The third module of the proposed equipment selection model rep-resents another major novelty of the current paper,as it aspires to respond to a problem rooted both in practice and in recently sug-gested selection models.This problem is the integration of soft and hard factors,or in other words,how to evaluate the cumula-tive effect of intangible,soft factors in comparison with costs, even after that effect has been converted into a quantitative mea-sure.This task is essentially an AHP-based bene?t-cost analysis and it appears to be even more challenging from a conceptual viewpoint?though not from a technical one?than the task tackled by the second module,given the substantial limitations of the few previous attempts at solving the problem,as well as the dif?culty to ensure that the solution is“right,”i.e.,both practical and ana-lytically sound.
Previous Solutions
Three different solutions have been suggested for an AHP-based bene?t-cost analysis,as follows.
1.Fong and Choi?2000?;and Cheung et al.?2001b?:The origi-
nal AHP method,according to which the hierarchy is initially constructed so that Level2includes both qualitative and cost factors.In disciplines in which costs traditionally constitute the major key factor,this mixture of factors appears to be problematic,mainly due to the dif?culties expected in pair-wise comparisons between cost and noncost factors,since costs are likely to be assigned relative weights that are ex-cessively high.
2Hastak and Halpin?2000?:Cost estimates and evaluation of bene?ts are performed separately,and bene?t-to-cost ratios are then computed for each alternative?the higher the quo-tient,the better the alternative?.The drawback of this method is that it has no capacity for distinguishing between alterna-tives that exhibit the same bene?t-to-cost ratio but vary in the difference between costs and bene?ts.Hence this method is suitable only for cases in which cost differences between alternatives are relatively small.
3Skibniewski and Chao?1992?:This method is similar to the ?rst method,but Level2in the hierarchy contains only two groups of selection criteria:costs and bene?ts.This method suffers from what appears to be a problem that is inherent in AHP for sets containing only two attributes that are to be compared?i.e.,only one comparison is required?.The major limitation here is that the relative weighting of the costs and bene?ts cannot be reaf?rmed by additional pairwise compari-sons with other attributes in the set?CR computation is not applicable for a two-attribute set?.
Current Solution
Total evaluation of each alternative?see Fig.1?is performed by weighting its cost and its AHP-based bene?t score.The total score
representing the weighting process is obtained by a technique similar to AHP-based computation of priority vectors,i.e.,it is the sum of two products:?1?cost weight multiplied by cost score,W c ?S c ;and ?2?bene?t weight multiplied by bene?t score,W b ?S b .Since the total score comprises two components,costs and bene?ts,the sum of their respective weights is always 1.0.Note that unlike with bene?t scores,the higher the cost of an alternative,the lower its cost score ?see Eq.?4?and subsequent computation example ?.
The determination of cost and bene?t weights is guided by the following principles.
?If the costs of the alternatives are similar,the decision will be based solely on the bene?ts.In this case,the weight of the bene?ts will be 1.0,and that of the costs 0.0.The alternative with the highest bene?t score will be the one selected.
?If the costs of the alternatives differ signi?cantly from each other ?with signi?cance margins de?ned by the user so that no matter how high the bene?ts are,they cannot shift the balance towards a high-cost alternative ?,the decision will be based solely on the costs.In this case,the weight of the bene?ts will be 0.0,and that of the costs 1.0.The alternative with the low-est cost will be the one selected.This principle is already re?ected in the selection algorithm,as outlined in Fig.1,in which bene?t evaluation is skipped.
?For any other situation,i.e.,the cost difference between the alternatives is neither negligible nor decisively great,the weights will be determined in linear proportion to the differ-ence.In other words,the greater the difference,the bigger the weight of the costs,W c ,and the smaller the weight of the bene?ts,W b ,with W c +W b =1.0.If the cost difference for which W b =0is denoted as ?C max ,and the cost difference between two alternatives i and j is ?C ij ,?C ij ??C max ,then
W c =?C ij /?C max
?2?W b =1.0??C ij /?C max ?=1.0?W c ?
?3?
This solution is based on the supposition that the construction company will be willing to pay a certain sum of money for ben-e?ts that are not re?ected in the cost estimates.In other words,the company will favor a higher-cost alternative over a lower-cost one,if the former has advantages the latter lacks.This supposition was supported by a study of current practices of equipment selec-tion ?Goldenberg 2002?,and was further con?rmed by the senior project managers who participated in the testing of the current model ?see “Model Implementation and Testing”?.Thus at one end of the scale are two ?or more ?alternatives with “near enough the same costs,”in which case “decisions may depend entirely on an adequate assessment of intangible factors”?Illingworth 1993?,while at the other end of the scale is a maximum cost difference,
above which the company will favor the lower-cost alternative.Between these two extremes,prorating appears to be the most logical solution.
Fig.4presents a zoom-in on the Total Evaluation Module,showing the ?owchart of the aggregation ?of costs and soft fac-tors ?algorithm.The cost score,S c ,of each alternative is deter-mined objectively ?i.e.,with no need for user judgment ?by com-puting what fraction of the sum total of the reciprocal cost values of the alternatives is represented by the alternative’s reciprocal cost value,1/C .Thus,for alternative i of N alternatives,the cost score is given by
S c ,i =
1/C i
?k =1N
1/C k
?4?
Computation Example
Table 2summarizes the input and output for the computation of cost and bene?t aggregation in a case with three alternatives.The cost of each equipment option,as calculated for the example,is given in column 2,and the AHP-based bene?t scores computed are given in column 5.To complete the input data,it is assumed that the project management team has set the sum of $1.32M as the maximum difference between the highest-cost option and the lowest-cost one,for a case in which the former option is favored over the latter for the bene?ts it offers ?i.e.,for ?C max =$1.32M,W b =0?.This sum was set,as a management decision,at 2%of the overall cost of the project.
Following are the steps required to obtain the output ?with reference to column numbers in Table 2?:
1.Normalized cost scores ?column 3?are computed using
Eq.?4?;
2.The cost score of the lowest-cost alternative,Alt.3,is set as
Table 2.Example Computation of Total Scores
Alternative Cost ?M dollars ?
C Cost score
Bene?t score Cost weight W c Bene?t weight W b Total score
Normalized
S c Relative to Alt.3Normalized
S b Relative to Alt.3Relative to Alt.3Normalized
123456789101 1.800.3410.9240.450 1.4020.1060.894 1.3510.4372 2.120.2900.7860.2290.7130.3480.6520.7380.2393
1.66
0.369
1.000
0.321
1.000
0.000
1.000
1.000
0.324
Fig.4.Aggregation of costs and soft factors
1.000,and the other two cost scores?from column3?are
adjusted proportionally to it?column4?;
3.The same alternative,Alt.3,is also set as a benchmark for
bene?t scores;its bene?t score is thus1.000and the other two bene?t scores?column5?are adjusted proportionally to it?column6?;
4.Cost weights?column7?are computed using Eq.?2?;
5.Bene?t weights?column8?are computed using Eq.?3?;
6.Total scores relative to Alt.3are computed by summing
the products obtained by multiplying scores by their respec-tive weights?column9=column4?column7+column6?column8?;and
7.The relative total scores in column9are normalized so that
their sum equals1.0?column10?.
Alt.1received the highest total score,and was therefore the
selected equipment option in this example.
Sensitivity Analysis
The identi?cation of situations in which a slight modi?cation of
user preferences might lead to the selection of a different alterna-
tive is vital,since user input is not always absolute and?nal.Thus
a sensitivity analysis can detect weak spots in the solution,and a
revision of the solution may be entailed.
Model Implementation and Testing
The proposed model was implemented in Microsoft Excel.The
computerized system provides a template for all user input?hier-
archy construction,pairwise comparisons,assessment of alterna-
tives,and maximal cost difference between alternatives for total
evaluation?,as well as a tool for all subsequent computations ?priority vectors,consistency ratios,aggregation of relative weights,cost scores,and?nal scores?.The use of designated AHP
software?Expert Choice2001?was considered for the Bene?t
Evaluation Module,but the development of a complete,stand-
alone system capable of executing all tasks of the proposed
model,tailored to the speci?c needs of the envisioned equipment
selection process,and suited for practical use,was preferred.
The system was tested on four senior project managers,each
with experience in the construction management of complex,
large-scale,and equipment-intensive projects utilizing advanced
construction methods.Over the years,these four referees had
worked for six of the leading construction companies in the coun-
try?D&B2003?and they therefore represent not only different
personal judgment and professional attitudes,but also different
planning cultures and traditions.
A test model project was developed after six real projects that
served as case studies of equipment selection considerations?Sha-
pira and Goldenberg2005?.These projects—public,commercial,
and residential complexes—consisted of high-rise tower buildings
erected on lower horizontal structures,and exhibited extensive
use of construction equipment?all kinds and con?gurations of
tower cranes,concrete pumps,placing booms,state-of-the-art
forming systems,and more?.Similarly,the model project was a
160m high structure comprising a47-?oor tower erected on top
of three lower horizontal commercial?oors.This building com-
plex was set in a con?ned site,located in a busy urban area and in
proximity to congested throughways.
Using the model,and with a strong emphasis on practice,an-
swers were sought to the following questions.
?Can qualitative,soft factors—as handled by the model—affect
the selection process so that a higher-cost option is favored over a lower-cost one?
?Would the proposed system yield the same results?i.e.,selec-tion of the same equipment option?as are obtained intuitively by the experienced,competent,project manager,without using the system?
?Does the proposed selection model suit the potential user in terms of professional attitude,mind-set,and working style?Is the computerized tool friendly and accommodating?Is the sys-tem,overall,practical?
Each of the referees was presented with three complete equipment options that were generated and processed down to the last detail,including cost estimates and schedules.The alter-natives,differing in the type and location of equipment,as well as in regard to day versus night work and cast-in-place versus precast concrete,were all equivalent in terms of all-inclusiveness, overall construction duration,and postulations regarding cost estimates.
Answers to all the above-listed questions were persuasively af?rmative;all four referees,each from a different background:?Accustomed themselves with the system in a matter of min-utes,found it friendly and convenient,and commended it for directing them towards structured and methodical thinking;?Arrived at the very same solution they had arrived at intu-itively,based on their own personal experience with previous similar projects;
?Favored an equipment option that was not the least costly of the three;and
?Found the model to be comprehensive,reliable,and practical.
Table3summarizes the testing of the system by the referees. Note that the second choice of three referees?Nos.2,3,and4?is the lowest-cost alternative?Alt.3?,while one referee?No.1?fa-vors the highest-cost alternative?Alt.2?over the lowest-cost one as the second choice.The four referees also differ with regard to score differentiation:in three cases?Nos.1,2,and4?the selected alternative?Alt.1?leads signi?cantly over the second choice, while in one case?No.3?the difference is marginal.Variegated results in terms of ranking and score differentiation are yet an-other indication of the system’s capacity to accommodate differ-ent intuition and judgment stemming from different experience and traditions.
Conclusion
The proposed equipment selection model purports to offer a com-prehensive solution for the systematic evaluation of qualitative decision factors alongside a mechanism for the overall integrative evaluation of hard and soft factors.The model has the capacity to handle a great number of different criteria in a way that truly Table3.Results of Model Testing
Referee
Intuitive
selection
Final scores and system-supported selection a
Alt.1
?cost:
1.80M dollars?
Alt.2
?cost:
2.12M dollars?
Alt.3
?cost:
1.66M dollars?1Alt.10.470.280.25
2Alt.10.440.240.32
3Alt.10.360.300.34
4Alt.10.470.240.29
a Selected alternative denoted by boldface.
re?ects the complex reality,yet without losing its practicality.The model incorporates the context and unique conditions of the project and allows for manifestation of user experience and sub-jective perception,all while securing a framework for a structured process and assuring solution consistency.The model was imple-mented in an in-house developed,stand-alone system that was improved and validated through rigorous testing by senior profes-sionals representing a wide spectrum of planning cultures and personal judgment.
The modi?cation of AHP to correspond with the nature of equipment selection and the ensuing model development included several main features:?1?offsetting of the rank reversal phenom-enon;?2?convenient and ef?cient evaluation of alternatives at the lowest hierarchy level;?3?a novel method for the integration of cost scores with AHP-based bene?t scores;?4?guidelines for hi-erarchy construction and for the determination of relevant deci-sion factors;and?5?computerized modules for the analytic, model-speci?c computations.
The proposed model was developed,among other things,in light of limitations found in recently introduced equipment selec-tion models.As such,it
?Addresses the entire site plant and allows for the evaluation of any building construction equipment option;it is not limited to the solution of certain subproblems?e.g.,cranes only?;?Allows for the systematic consideration of unlimited numbers or classes of soft and hard factors;
?Can accommodate both owned and rented equipment?as part of the same alternative?,as long as these are duly considered in the Cost Evaluation Module;
?Enables the tracing of user preferences and the analysis of decisions made throughout the selection process?no“black box”approach?;and
?Does not necessitate simpli?cation of the problem as a prereq-uisite for reaching a solution.
AHP,the core of the proposed model,is an analytic method. At some point in the course of the solution,qualitative eval-uations must therefore be converted into numbers.This,indeed, has drawn some criticism,following which new studies further cemented the theoretical base of AHP,so much so that AHP,both as a philosophy and as a decision-making tool,appears to be a viable option for bridging the tension that exists between two worlds:the vast body of tangible and intangible,often con-tending,hard and soft factors on the one hand,and the need to select a single equipment option on the other hand;an open-ended,ill-de?ned starting point versus an absolute,deterministic ?nal decision.
On the more practical side,the AHP-based model has proven to be a convenient and user-friendly tool,particularly given the relatively complex process it is applied to here,its built-in facility to force the user into orderly,methodical thinking,and its inher-ent capacity to unveil the tacit knowledge of the competent,ex-perienced user.The model,therefore,constitutes an effective means for formalization of knowledge,which may itself be con-sidered to be one of the current study’s primary contributions.It is,however,more than a shell into which knowledge is poured,in that it provides a categorized list of selection criteria and a well-tested hierarchy,which along with the AHP method,guide and assist the user in making sound,logical decisions.As such,the model may also serve to train the novice engineer,particularly given its transparency with regard to the evolution of the selection process and the considerations made.
Further research is needed to develop better guidelines for management teams faced with the need to decide on the price they are willing to pay for bene?ts that are not directly re?ected in cost estimates.This issue relates to aspects of the project that are far beyond equipment selection,and therefore appears to be complex and challenging.On the more technical side,the pro-posed model may be augmented by combining it with advanced decision-aid tools for optimal site organization and equipment location,as well as with state-of-the-art cost estimation models covering all phases of equipment use on the project.Finally, much can be gained from studying the proposed model’s actual assimilation in construction companies and its long-term applica-tion in real-life projects.
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Manage.,125?2?,115–122.
第四章 等效电路模型参数在线辨识 通过第三章函数拟合的方法可以确定钒电池等效电路模型中的参数，但是在实际运行过程中模型参数随着工作环境温度、充放电循环次数、SOC 等因素发生变化，根据离线试验数据计算得到的参数值估算电池SOC 可能会造成较大的估计误差。因此，在实际运行时，应对钒电池等效电路模型参数进行在线辨识，做出实时修正，提高基于模型估算SOC 的精度。 4.1 基于遗忘因子的最小二乘算法 参数辨识是根据被测系统的输入输出来，通过一定的算法，获得让模型输出值尽量接近系统实际输出值的模型参数估计值。根据能否实时辨识系统的模型参数，可以将常用的参数辨识方法分为离线和在线两类，离线辨识只能在数据采集完成后进行，不能对系统模型实时地在线调整参数，对于具有非线性特性的电池系统往往不能得到满意的辨识结果；在线辨识方法一般能够根据实时采集到的数据对系统模型进行辨识，在线调整系统模型参数。常用的辨识方法有最小二乘法、极大似然估计法和Kalman 滤波法等。因最小二乘法原理简明、收敛较快、容易理解和掌握、方便编程实现等特点，在进行电池模型参数辨识时采用了效果较好的含遗忘因子的递推最小二乘法。 4.1.1 批处理最小二乘法简介 假设被辨识的系统模型： 12121212()()()1n n n n b z b z b z y z G z u z a z a z a z ------+++==++++L L （4-1） 其相应的差分方程为： 1 1 ()()()n n i i i i y k a y k i b u k i ===--+-∑∑（4-2） 若考虑被辨识系统或观测信息中含有噪声，则被辨识模型式（4-2）可改写为： 1 1 ()()()()n n i i i i z k a y k i b u k i v k ===--+-+∑∑（4-3） 式中， ()z k 为系统输出量的第k 次观测值；()y k 为系统输出量的第k 次真值，()y k i -为系统输出量的第k i -次真值；()u k 为系统的第k 个输入值，()u k i -为 系统的第k i -个输入值；()v k 为均值为0的随机噪声。
龙源期刊网 https://www.doczj.com/doc/155479713.html, 温室蔬菜常见病虫害及预防措施 作者：赫素真 来源：《现代园艺·园林版》2017年第06期 摘要：主要针对温室蔬菜常见病虫害及预防措施进行研究。 关键词：温室蔬菜;病虫害;预防措施 1常见的病害与化学防治 1.1灰霉病 灰霉病是一种比较常见的病害，主要危害的蔬菜是辣椒、茄子类，这种病害是真菌感染引起，且不同的危害部位有不同的表现现状，像蔬菜叶子会发生颜色的变化，会由绿变灰白，且腐烂，果实在初始状态，直接腐烂，腐烂处会出现灰霉，最后果实脱离组织掉落。温室中的潮湿环境极易滋生真菌，所以要控制好温室的含水量。此外，在发病时，要及时对其进行清理，避免此真菌对其他的正常蔬菜产生影响。在进行化学防治时，可以用成分为嘧霉胺，含量不少于70%，剂型为水分散粒剂，用量为9（g/ha）的绿亨1000～1500倍液体进行有效预防。 1.2根腐病 根腐病主要作用于蔬菜的根部，导致根部直接腐烂，且死亡。根腐烂的根本原因还是温室里的水分太多了，又得不到充足的太阳光照射，或者根部生长的土壤环境中土结块或者土中的含水量过多，促使真菌滋生的条件。所以对其进行预防，使其生长的环境和周围的环境保持水分适量。在进行化学防治时，可以采用绿亨1号5000～6000倍液防治。 1.3霜霉病 霜霉病发病时期不确定，主要作用于蔬菜叶，主要表现现状是在叶背呈现霜霉，在正面呈现斑点，同时叶的正反面颜色逐渐褪为黄色。温室环境温度过高、空气中的水分含量较大，以及蔬菜之间的间距没有控制在合理的范围内，都会为滋生相关的病菌提供条件。所以对其进行预防时，针对病菌生存的环境，逐一改变。在进行化学治疗时，可以采用含量为72%、有效成分为霜脲、锰锌的可湿性粉剂防治。 1.4疫病 温室大棚的特点就是温度高、湿度大、严密性强，这样有助于相关蔬菜疫病的产生，主要受影响的蔬菜有茄子、辣椒等。在进行预防时，就要对大棚进行定时的通风换气，同时平衡一下大棚内温度和湿度，还要使其接受一定时间的日光照射，以便进行光合作用，促进蔬菜生
番茄病虫害防治 一、主要病害及防治 1.番茄灰霉病 防治措施主要是采用以药剂防治为主，结合生态防治的综合防治。在配好的蘸花药液里加入保果宁或50％利得（异菌.福)可湿性粉剂进行蘸花或喷花。加强通风换气，调节温、湿度，避免结露。苗期对床土表面进行灭菌，收获后和种植前彻底清除温室内病残体并集中烧毁或深埋，并对棚室进行消毒处理。 2.番茄菌核病 防治措施主要采用栽培措施和药剂防治相结合。深翻地，将菌核翻至10厘米以下，使其不能萌发；在夏季高温季节利用换茬时用稻草、生石灰或马粪等混合耕地，做起垄小畦，灌满水后铺上地膜，密闭大棚，使土温升高，保持20天。可杀死病菌；加强管理，及时摘除老叶、病叶，清除田间杂草，注意通风排湿，采取滴灌、暗灌；药剂防治在发病初可喷洒40％菌核净可湿性粉剂500倍液或50％农利灵（乙烯菌核利）可湿性粉剂1000～1500倍液等。棚室采用烟雾法或粉尘法施药，亩用10％速克灵（腐霉利）烟剂250～300克，也可于傍晚喷撒6.5％甲霉灵粉尘、百菌清粉尘剂或10％灭克粉尘剂，每亩次1公斤。 3.番茄晚疫病 防治措施主要通过选用耐病品种，结合药剂进行综合防治措施。植株茎、叶茸毛多的品种较耐晚疫病。施足底肥，增施磷、钾肥，提高植株抗病力。在发现中心病株后，要立即全面喷药，集中消灭发病中心，发病中心地块要反复3～4次喷药封锁，并将病叶、病枝、病果和重病株带出田外烧毁。药剂可选用50％安克可湿性粉剂1500～2000倍液或65％安克锰锌可湿性粉剂600～800倍液等。 4.番茄叶霉病 防治措施要以抗病品种为主，结合栽培措施，辅以药剂控制的综合防治措施。选用抗病品种佳粉18号、硬粉8号、仙克1号，金棚1号，中杂105，京丹4、6号，京丹绿宝石和京丹黄玉等对叶霉病菌1.2.3和1.2.3.4生理小种群高抗；栽培措施上，前期作好保温，后期加强通风，降低湿度，防止叶面结露。及时整枝打杈，去掉老病叶；在发病初期可用45％百菌清烟剂每亩250～300克，熏一夜或于傍晚喷撒7％叶霉净粉尘剂，或5％百菌清粉尘剂或10％敌托粉尘剂，每亩1公斤，隔8～10天1次，连续或交替轮换施用。 5.番茄溃疡病 防治措施要采取严格的种子检疫措施，加强栽培管理，结合药剂防治的综合防治措施。对种子进行温汤浸种或药剂浸种，催芽播种；采用高垄栽培。及时清除病株并销毁，病穴要进行生石灰消毒。避免偏施氮肥，禁止大水漫灌，整枝打杈时避免带露水操作；发病时，全田喷洒14％络氨铜水剂300倍、或77％可杀得可湿性微粒粉剂500倍液等。 6.番茄花叶病毒 防治措施上采取以抗病品种为主，结合栽培措施的综合防治。目前国内推广的番茄品种多数具有抗番茄花叶病毒的能力。避蚜防病，大棚采用网纱覆盖风口或利用银灰膜反光，减少室外蚜虫进入，以减轻病毒病的发生。拔除病苗，田间整枝打杈时要病、健株分开操作，
用GARCI模型预测股票指数波动率 目录 Abstract ......................................................................... 1.引言........................................................................... 2.数据........................................................................... 3.方法........................................................................... 3.1.模型的条件平均............................................................ 32模型的条件方差............................................................... 3.3预测方法.................................................................... 3.4业绩预测评价............................................................... 4.实证结果和讨论................................................................. 5.结论........................................................................... References ....................................................................... Abstract This paper is designed to makea comparison between the daily conditional varianee through seven GRAChhodels. Through this comparison, to test whether advaneed GARCH models are outperform ing the sta ndard GARCH models in predict ing the varia nee of stock in dex. The database of this paper is the statistics of 21 stock in dices around the world from 1 January to 30 November 2013. By forecast ing one —step-ahead con diti onal varia nee within differe nt models, the n compare the results within multiple statistical tests. Throughout the tests, it is found that the sta ndard GARCH model outperforms the more adva need GARCH models, and recomme nds the best
浅析电力系统模型参数辨识 （贵哥提供） 一、现状分析 随着我国电力事业的迅猛发展, 超高压输电线路和大容量机组的相继投入, 对电力系统稳定计算、以及其安全性、经济性和电能质量提出了更高的要求。现代控制理论、计算机技术、现代应用数学等新理论、新方法在电力系统的应用，正在促使电力工业这一传统产业迅速走向高科技化。 我国大区域电网的互联使网络结构更复杂，对电力系统安全稳定分析提出了更高的要求，在线、实时、精确的辨识电力系统模型参数变得更加紧迫。由于电力系统模型的基础性、重要性，国外早在上世纪三十年代就开始了这方面的分析研究，[1,2]国内外的电力工作者在模型参数辨识方面做了大量的研究工作。[3]随后IEEE相继公布了有关四大参数的数学模型。1990年全国电网会议上的调查确定了模型参数的地位，促进了模型参数辨识的进一步发展，并提出了研究发电机、励磁、调速系统、负荷等元件的动态特性和理论模型，以及元件在极端运行环境下的动态特性和参数辨识的要求。但传统的测量手段，限制了在线实时辨识方法的实现。 同步相量测量技术的出现和WAMS系统的研究与应用，使实现在线实时的电力系统模型参数辨识成为可能。同步相量是以标准时间信号GPS作为同步的基准，通过对采样数据计算而得的相量。相量测量装置是进行同步相量测量和输出以及动态记录的装置。PMU的核心特征包括基于标准时钟信号的同步相量测量、失去标准时钟信号的授时能力、PMU与主站之间能够实时通信并遵循有关通信协议。 自1988年Virginia Tech研制出首个PMU装置以来，[4]PMU技术取得了长足发展，并在国内外得到了广泛应用。截至2006年底，在我国范围内，已有300多台P MU装置投入运行，并且可预计，在不久的将来PMU装置会遍布电力系统的各个主要电厂和变电站。这为基于PMU的各种应用提供了良好的条件。 二、系统辨识的概念 系统模型是实际系统本质的简化描述。[5]模型可分为物理模型和数学模型两大类。物理模型是根据相似原理构成的一种物理模拟，通过模型试验来研究系统的
灵台县无公害蔬菜常见病虫害防治技术 一、十字花科类蔬菜主要病害 白菜软腐病症状：白菜软腐病又称白菜腐烂病、烂疙瘩、酱桶、脱帮等，属细菌性病害一般通过雨水和灌溉水传播,病菌只有与白菜伤口接触,才能侵染发病。多从白菜包心期开始发病，先在菜帮基部出现半透明状浸润斑，逐渐扩大为灰白色，嫩组织腐烂，老组织干缩。后期，由叶帮基部向短缩茎发展，引起根髓腐烂，并溢出灰黄色粘液。植株外叶中午萎蔫，早晚恢复，持续几天后，病株外叶平贴地面，心叶或叶球部分外露，叶柄茎或根茎处髓组织溃烂，流出灰褐色或白色粘稠状物，并散出臭味，轻碰病株即倒折溃烂。潜伏有病菌的组织，入窖后可发生腐烂。较坚实少汁的组织受害时，也是先呈水浸状，逐渐腐烂，但最后病部组织干缩，变为干腐状。此病发生危害期长，在田间、贮运期及市场上都能发生腐烂，造成重大损失。在包心期，遇低温多雨，地势低洼，排水不良时发病重。除白菜外，甘蓝、萝卜等受害也较重。此病除危害十字花科蔬菜外，还可危害马铃薯、番茄、辣椒、洋葱、胡萝卜、芹菜、莴苣等多种蔬菜。 防治：可用72%农用链霉素2000-3000倍，每隔7-10天喷一次，连喷2-3次。 白菜霜霉病症状：真菌性病害，主要发生在叶部。先从外部叶片开始，在病叶正面出现淡黄色小病斑，逐渐扩大成黄绿色不规则病斑。天气潮湿时，叶背面产生白色霜层，气候干旱时，病斑干枯。病原为鞭毛菌寄生霜霉真菌。田间高温多雨是病害常严重发生。早播、播种过密、通风不良、连茬、包心期缺肥易引发病害。 防治：喷施1:0.5:240的波尔多液或75％百菌清可湿性粉剂800倍液、72%霜脲锰锌（杜邦克露、克抗灵、克霜氰）800～1000倍液、每5～7天喷1次。霜霉病的发生与病毒病关系密切，防治应综合考虑。 二、茄科类蔬菜主要病害 茄科晚疫病症状：又称茄科疫病，是番茄、辣椒主要病害之一，该病主要危害叶片及果实，也可危害茎部。一般是中下部叶片先发病。叶片发病首先在叶尖，叶缘处出现不规则暗绿色水渍状病斑，然后病斑扩大并变成褐色。在高湿条件下，病势发展迅速，叶背面的病，病健交界处发生白色霉状物。在干燥条件下，病部干枯，呈清白色，脆而易碎。危害茎部的病斑开始呈暗绿色，后变成黑褐色，稍凹陷，腐败状，病部边缘生有较重的白色霉层，最后表皮腐烂，植株易从腐烂处弯折。果实多发病在青果期表面开始呈灰绿油渍状硬斑块，逐渐变为暗褐色或棕褐色，病斑稍凹陷，边缘
用GARCH模型预测股票指数波动率 目录Abstract (2) 1.引言 (3) 2.数据 (6) 3.方法 (7) 3.1．模型的条件平均 (7) 3.2.模型的条件方差 (8) 3.3预测方法 (9) 3.4业绩预测评价 (9) 4.实证结果和讨论 (12) 5.结论 (16) References (18)
Abstract This paper is designed to make a comparison between the daily conditional variance through seven GRACH models.Through this comparison,to test whether advanced GARCH models are outperforming the standard GARCH models in predicting the variance of stock index.The database of this paper is the statistics of21stock indices around the world from1January to30 November2013.By forecasting one–step-ahead conditional variance within different models, then compare the results within multiple statistical tests.Throughout the tests,it is found that the standard GARCH model outperforms the more advanced GARCH models,and recommends the best one-step-ahead method to forecast of the daily conditional variance.The results are to strengthen the performance evaluation criteria choices;differentiate the market condition and the data-snooping bias. This study impact the data-snooping problem by using an extensive cross-sectional data establish and the advanced predictive ability test.Furthermore,it includes a13years’period sample set, which is relatively long for the unpredictability forecasting studies.It is part of the earliest attempts to inspect the impact of the market condition on the forecasting performance of GARCH models.This study allows for a great choice of parameterization in the GARCH models,and it uses a broad range of performance evaluation criteria,including statistical loss function and the Mince-Zarnowitz regressions.Thus,the results are more robust and diffusely applicable as compared to the earliest studies. KEY WORDS:GARCH models;volatility,conditional variance,forecast,stock indices.
基于最小二乘模型的Bayes 参数辨识方法 王晓侃1，冯冬青2 1 郑州大学电气工程学院，郑州（450001） 2 郑州大学信息控制研究所，郑州（450001） E-mail ：wxkbbg@https://www.doczj.com/doc/155479713.html, 摘 要：从辨识定义出发,首先介绍了Bayes 基本原理及其两种常用的方法,接着重点介绍了基于最小二乘模型的Bayes 参数辨识,最后以实例用MATLAB 进行仿真,得出理想的辨识结果。 关键词:辨识定义；Bayes 基本原理；Bayes 参数辨识 中国图书分类号：TP273+.1 文献标识码：A 0 概述 系统辨识是建模的一种方法。不同的学科领域，对应着不同的数学模型，从某种意义上讲，不同学科的发展过程就是建立它的数学模型的过程。建立数学模型有两种方法:即解析法和系统辨识。L. A. Zadehll 于1962年曾对”辨识”给出定义[1]:系统辨识是在对输入和输出观测的基础上，在指定的一类系统中，确定一个与被识别的系统等价的系统。一般系统输出y(n)通常用系统过去输出y(n-m)和现在输入u(n)及过去输入u(n-m)的函数描述 y(n)=f(y(n-1),y(n-2)，...，y(n-m y ), u(n),u(n-1),... ,u(n-m u ))=f(x(n),n) x(n)=[y(n-1),y(n-2),...y(n-m y ), u(n),u(n-1),...,u(n-m u )]’ 这里f(,)为未知函数关系，一般情况为泛函数，可以是线性函数或非线性函数，分别对应于线性或非线性系统，通常这个函数未知，但是局部输入输出数据可以测出，系统辨识的任务就是根据这部分信息寻找确定函数或确定系统来逼近这个未知函数。但实际上我们不可能找到一个与实际系统完全等价的模型。从实用的角度来看，系统辨识就是从一组模型中选择一个模型，按照某种准则，使之能最好地拟合由系统的输入输出观测数据体现出的实际系统的动态或静态特性。接下来本文就以最小二乘法为基础的Bayes 辨识方法为例进行分析介绍并加以仿真[4]。 1 Bayes 基本原理 Bayes 辨识方法的基本思想是把所要估计的参数看做随机变量,然后设法通过观测与该参数有关联的其他变量,以此来推断这个参数。 设μ是描述某一动态系统的模型，θ是模型μ的参数，它会反映在该动态系统的输入输出观测值中。如果系统的输出变量z(k)在参数θ及其历史纪录(1) k D ?条件下的概率密度函 数是已知的，记作p(z(k)｜θ，(1) k D ?)，其中(1) k D ?表示（k-1）时刻以前的输入输出数据集 合，那么根据Bayes 的观点参数θ的估计问题可以看成是把参数θ当作具有某种先验概率密 度p （θ，(1) k D ?）的随机变量，如果输入u(k)是确定的变量，则利用Bayes 公式，把参数θ 的后验概率密度函数表示成[2] p (θ，k D ）= p (θ｜z (k ),u(k ), (1) k D ?)=p (θ｜z (k ),(1) k D ?) = (k-1) (k-1) p(z(k)/,D )p(/D ) (k-1)(k-1)p(z(k)/,D )p(/D )d θθθθθ∞∫?∞ (1) 在式(1)中,参数θ的先验概率密度函数p(θ｜(1) k D ?)及数据的条件概率密度函数p(z(k)｜θ，
蔬菜——常见虫害防治的化学农药 1、菜青虫：311菜蛾敌600-1000倍液、2.5%功夫乳油2000-5000倍液、2.5%敌杀死乳油2000-5000倍液、5%卡死克乳油2000-3000倍液、5%抑太保乳油2000-3000倍液、50%辛硫磷乳油1000倍液、10%天王星乳油1000倍液、21%灭杀毙乳油3000倍液。注意：抑太保要比一般农药提早3天施药。 2、小菜蛾：311菜蛾敌600-1000倍液、2.5%功夫乳油2000-5000倍液、2.5%敌杀死乳油2000-5000倍液、5%卡死克乳油2000-3000倍液、5%抑太保乳油2000-3000倍液、5%锐劲特悬浮剂3000倍液、5%农梦特2000倍液。 3、斜纹夜蛾、甜菜夜蛾、银纹夜蛾、甘蓝夜蛾：25%辛硫灭扫利乳油2500-4000倍液、夜蛾灵800-1200倍液、夜蛾净1500-2000倍液、克蛾宝2000-3000倍液、10%氯氰菊酯乳油2000-5000倍液、2.5%功夫乳油5000倍液、2.5%木虱净乳油1500倍液。注意：辛硫灭扫利、夜蛾灵、夜蛾净不能与碱性药混用。 4、黄曲条跳甲成虫、幼虫：80%敌百虫可湿性粉剂800-1000倍液、农地乐1000-1500倍液、扑甲灵600-1000倍液、50%辛硫磷乳油1000倍液、21%增效氰乌乳油4000倍液。注意：农地乐应注意标签说明。喷施扑甲灵时应从田块四周向中间喷药，防止虫逃跑。 5、蚜虫（瓜蚜）：快杀灵3000-5000倍液、一遍净3000-5000倍液、杀灭菊酯8000-10000倍液、灭百可5000-8000倍液、40%乐果乳油1000-2000倍液、10%大功臣可湿性粉剂2500倍液。注意：快杀灵、杀灭菊酯不与碱性药混用。盛装灭百可的容器要冲洗3次后埋掉。 6、红蜘蛛：73%克螨特乳油3000-5000倍液、爱福丁3000-4000倍液、5%卡死克乳油1000-1500倍液、20%螨克乳油1000-1500倍液。 7、黄守瓜、瓜绢螟：80%敌百虫可湿性粉剂800-1000倍液、50%辛硫磷乳油1000-1500倍液、10%氯氰菊酯2000-5000倍液、灭杀毙8000倍液。注意：瓜豆对50%辛硫磷敏感，高温慎用。 8、蓟马：爱卡士1000-2000倍液、拉硫磷1500-2000倍液、0%辛硫磷乳油1000-2000倍液、0%氯马乳油1000-2000倍液、0%乐果乳油1000倍液。注意：20%氯马乳油不与碱性药混用。 9、瓜食蝇：2.5%溴氰菊酯乳油2000-3000倍液、香蕉或菠萝皮40份，加80%敌百虫晶体（其它农药）0.5份，加香精1份，加水调成糊状毒饵诱杀。每亩20个点，每点25克、50%敌敌畏乳油1000倍液。 10、潜叶蝇：2.5%溴氰菊酯1500-3000倍液、爱卡士1000-2000倍液、抑太保乳油 2000-3000倍液、21%灭杀毙乳油8000倍液、25%喹硫磷乳油1000倍液、80%敌百虫可湿性粉剂1000倍液。 11、豆秆蝇：⑴50%辛硫磷乳油1000倍液。⑵2.5%溴氰菊酯1500-3000倍液。注意：用时应清除
基于GARCH模型的沪深300指数收益率波动性分析
摘要 股票市场的价格波动研究，不仅具有重要的学术意义，而且具有重要的实际意义。股价的波动给投资者带来了获利的机会。因此，金融市场的波动性一直以来都是投资者和经济研究人员关注的焦点。 本文以对沪深300指数2005年1月4日到2014年6月11日每个交易日收盘价为原始数据，对其收益率进行了研究分析。研究结果表明：日收益率序列的波动表现出时变性、突发性和集簇性等特征。序列分布呈现出尖峰厚尾的特点，并且存在明显的GARCH效应，表明过去的波动对于未来的影响是持久的，同时也是逐渐衰减的。而且，沪深300指数波动幅度大。沪深300指数的频繁交易使得股指期货市场具有高流动性，这种高流动性也是造成指数波动的一大成因。 关键字：收益率；ARCH模型；GARCH模型
一、前言 1.1研究意义 股票市场的价格波动研究，不仅具有重要的学术意义，而且具有重要的实际意义。股价的波动给投资者带来了获利的机会。投资者可以通过对度量波动率来猜测股市的风险有多大，同时，了解波动性有助于投资者更好的理解和把我股票市场的运行规律，将股价界定在一个可能的范围内，当投资者认识到股价波动的规律，就可以帮助其做出明智的投资，以获取更多的利益。因此，金融市场的波动性一直以来都是投资者和经济研究人员关注的焦点。 1.2 研究对象和方法 沪深300指数是由上海和深圳证券市场中选取300只A股作为样本编制而成的成份股指数。沪深300指数选取规模大、流动性好的股票作为样本, 覆盖了沪深市场六成左右的市值,具有良好的市场代表性，所以有必要对其进行深入分析。 本文以中国金融期货交易所的沪深300指数2005年1月4日到2014年6月11日每个交易日收盘价为原始数据，共2288个数据样本。 就沪深300指数收益率的波动性研究方法而言，国内外的研究结果表明，许多金融时间序列都将GARCH模型作为解释金融数据的经验方法。因此，本文采用GARCH模型检验沪深300指数日收益率的波动性变化，希望可以发现沪深300指数的波动性特征。 二、GARCH模型介绍 2.1 ARCH模型 ARCH模型由Engle（1982）提出，并由Bollerslev（1986）发展成为GARCH-广义自回归条件异方差。这些模型广泛的应用与经济性的各个领域，尤其是金融时间序列中。 ARCH的核心是(1)式中t时刻的随机误差项ε的方差（σ2）依赖于t-1时刻的平方误差的大小，即依赖于ε2t?1。 Y t=β0+β1X1t+?+βk X kt+εt (1)
（一）黄瓜 一. 霜霉病疫病： 1.烯酰吗啉 2. 霜脲锰锌 3.菌霜杰 4.甲霜灵锰锌 二. 细菌性角斑病.叶枯病：1.中生菌素 2. 春雷霉素 3.噻唑锌 三. 白粉病黑星病：1. 乙嘧酚 2. 3.醚菌酯 4. 新秀戊唑醇 5. 白粉速净 6. 斑星杰 四. 炭疽病叶斑病：1. 苯醚甲环唑 2. 克菌杰 3.醚菌酯 五蔓枯病枯萎病根腐病：1. 枯草芽孢杆菌 2. 青枯立克 4. 中生菌素 5.络氨铜灌根 六. 灰霉病菌核病：1. 嘧霉胺 2. 丁子香酚 3. 统防统治 4. 异菌脲 5.腐霉利 6. 烟酰胺 (二）西瓜甜瓜茭瓜 一. 炭疽病.叶斑病：1.苯醚甲环唑 2. 克菌杰 3.斑星杰 4. 醚菌酯 5. 新秀戊唑醇 6.乙嘧酚 二. 蔓枯病枯萎病：1. 克菌杰 2. 青枯立克 3. 枯草芽孢
4. 中生菌素（上述产品喷雾或者少兑水拌成糊状涂抹病部） 6. 络氨铜灌根 三. 细菌性叶枯病.软腐病果腐病：1.中生菌素 2. 春雷霉素 3.噻唑锌 四. 疫病霜霉病：1.烯酰吗啉 2. 霜脲锰锌 3.菌霜杰 4.甲霜灵锰锌 五.病毒病：1. 菌毒清.吗啉胍 2. 氮苷.吗啉胍 3. 吗啉胍.乙酸铜 （三）番茄辣椒茄子 一. 晚疫病： 1.烯酰吗啉 2. 霜脲锰锌 3.菌霜杰 4. 甲霜灵锰锌 二.早疫病： 1. 苯醚甲环唑 2. 戊唑醇 3.醚菌酯 三. 灰霉病菌核病： 1. 嘧霉胺 2. 丁子香酚 3. 统防统治 4. 异菌脲 5.腐霉利 6. 烟酰胺 四. 叶霉病： 1. 春雷霉素 2. 新秀戊唑醇 3. 异菌脲
五病毒病： 1. 菌毒清.吗啉胍 2. 氮苷.吗啉胍 3. 吗啉胍.乙酸铜 六. 青枯病溃疡病茎基腐黄萎病枯萎病：1. 中生菌素 2. 青枯立克 3. 枯草芽孢杆菌 4.春雷霉素 5.络氨铜灌根 七. 细菌性叶斑病疮痂病：1.中生菌素 2. 春雷霉素 3.噻唑锌 （四）菜豆芸豆 一. 锈病炭疽叶斑病：1. 苯醚甲环唑 2.斑星杰 3. 新秀戊唑醇 4. 乙嘧酚 5. 克菌杰 6. 醚菌酯 二.细菌性疫病：1. 中生菌素 2 噻唑锌 三.花叶病毒病：1. 菌毒清.吗啉胍 2. 氮苷.吗啉胍 3. 吗啉胍.乙酸铜 四.枯萎病根腐病茎基腐：1.中生菌素 2.青枯立克 4. 枯草芽孢杆菌 5.络氨铜灌根
上海交通大学 硕士学位论文 Bouc-Wen滞回模型的参数辨识及其在电梯振动建模中的应用 姓名：周传勇 申请学位级别：硕士 专业：机械设计及理论 指导教师：李鸿光 20080201
Bouc-Wen滞回模型的参数辨识 及其在电梯振动建模中的应用 摘 要 电梯导靴是连接轿箱系统与导轨的装置，它能起到导向和隔振减振的作用。同时，在电梯的运行过程中它又将导轨由于制造或安装所造成的表面不平顺度传递给轿箱系统，从而引起轿箱系统的水平振动。国内外学者在电梯水平振动的建模和分析中，往往把导靴视为线性弹簧-阻尼元件来建模而忽略了非线性因素。事实上导靴与导轨之间存在非线性的迟滞摩擦力，本文通过实验的方法，采用Bouc-Wen 滞回模型来建立导靴-导轨非线性摩擦力模型。 Bouc-Wen滞回模型因其微分形式的非线性表达式而使得其参数辨识存在较大的困难，本文利用模型中部分参数的不敏感性，通过数学变换将非线性参数辨识问题转化为线性参数辨识问题，从而使得问题大大简化，参数辨识的效果也能满足要求。 基于以上导靴-导轨间摩擦力模型，本文进而建立了轿箱-导轨耦合水平振动动力学模型，该模型将轿箱系统等效为2自由度的平面运动刚体，将导靴等效为质量-弹簧-阻尼单元，同时考虑了导靴-导轨间的非线性摩擦力，以及导靴靴衬与导轨间接触的不连续性等。 在建立了轿箱-导轨耦合水平振动动力学模型后，利用Matlab/Simulink，建立了相应的仿真模型，开展了几种典型导轨不
平顺度激励（弯曲、失调和台阶）下的仿真分析。研究结果表明，这些分析对于电梯结构优化设计和动力学建模与分析有理论指导意义。 关键词：迟滞，参数辨识，非线性，动力学建模，系统仿真
主要蔬菜常见病虫害防治 日期：2013-10-14 10:40 作者：来源：湖南湘阴县农业局点击：2103 一、辣椒主要病虫害的防治 1、苗床期主要病虫害。①主要病害有灰霉病、炭疽病。分别用速克宁和甲基托布津防治，每隔15天喷一次药液。②主要虫害是蚜虫，可用敌蚜螨、克螨特等药物进行防治。 2、生长结果期的主要病虫害。病害主要有：真菌性疮痂病、炭疽病、疫病可选用可杀得或甲基托布津、瑞毒霉或百菌清防治；病毒病一般引起花叶、卷叶，可用病毒A或辣椒卷叶灵喷雾预防；白绢病、青病重在预防，加强土壤消毒，多施石灰。在发病初期分别5%多菌灵500倍液淋蔸进行防治。 二、茄子的主要病虫害防治 1、主要病害。①育苗初期易发生猝倒病和真菌性病害，防病药剂有速克宁、甲基托布津、百菌清等。 ②生长发育期后的病害有黄萎病、绵疫病。防治重在轮作，严格土壤消毒，重施生石灰。发病始期用可杀得喷雾防治。 2、主要虫害。主要有蚜虫、棉铃虫、十八星瓢虫、茶蟥螨等。蚜虫可用敌蚜螨、乐果等药防治；棉铃虫、十八星瓢虫用功夫或甲维盐等药防治；茶蟥螨用哒螨酮、克螨特防治。 三、白菜类病虫害防治 1、病害有很多，但发生最普遍、最严重的有软腐病、霜霉病和病毒病。①软腐病，发病初期及时拨除病株，并在周围土壤撒少量石灰；②霜霉病，发病初期可用以下药剂防治：75%甲霜灵800倍喷雾，40%乙磷铝150-200倍喷雾；③病毒病，苗期及时防治蚜虫，用吡蚜酮1000倍喷雾。 2、虫害的防治。虫害主要是菜青虫、甜菜夜蛾、蚜虫等虫害，可用敌杀死、甲维盐等药剂防治。 四、莴苣类病虫害防治 1、莴苣的病害主要有：霜霉病和软腐病。①霜霉病，在发病初期用25%甲霜灵800倍或45%代森铵900-1000倍喷施，每隔5-7天喷一次，连续2-3次。②软腐病，发病初期及时拨除病株，并在病穴四周撒少量石灰，同时，可用农链霉素200克/升或敌克松500-1000倍液或50%代森铵800-1000倍液喷施，每5-7天喷一次，连续2-3次。 2、莴苣的主要虫害是蚜虫和蜗牛。分别用敌蚜螨、蜗牛灵进行防治。 五、芹菜的病虫害防治 1、病害。苗期主要有猝倒病，发现病株，及时清除，并撒施药土。可用35%多菌灵拌细土25千克撒施，或用50%多菌灵1000倍液或用50%代森铵1000倍液或70%百菌清800倍液喷雾。成株病害主要有叶枯病和早疫病。可用70%代森锌可湿性粉剂500倍液、40%甲基托布津600-800倍液喷雾，隔5-7天交替用药。 2、主要虫害。芹菜虫害主要有斜纹夜蛾和蚜虫。斜纹夜蛾可用25%灭幼脲3号500倍液或功夫或甲维等药防治。蚜虫可用蚍蚜西酮、吡虫啉单剂防治。 六、黄瓜病虫害防治。 1、黄瓜的主要病害有枯萎病、疫病、霜霉病、细菌性角斑病，可分别用代森铵、可杀得、甲霜灵、农用链霉素防治。 2、虫害主要有黄守瓜虫、蚜虫等，可用敌蚜螨或拟除虫菊酯类等药物喷雾防治。
1.1.波动率 波动率是用来描述证券价格、市场指数、利率等在它们均值附近上下波动幅度的术语，是标的资产投资回报率的变化程度的度量。股票的波动率σ是用于度量股票所提供收益的不确定性。股票通常具有15%-50%之间的波动率。股票价格的波动率可以被定义为按连续复利时股票在1年内所提供收益率的标准差。当?t 很小时，2t σ?近似的等于在?t 时间内股票价格变化百分比的方差。这说明σ√?t 近似的等于在?t 时间内股票价格变化百分比的标准差。由标准差来表述股票价格变化不定性的增长速度大约为时间展望期长度的平方根（至少在近似意义下）。 1.2.由历史数据来估计波动率 为了以实证的方式估计价格的波动率，对股票价格的观察通常是在固定的时间区间内（如每天、每星期或每个月）。 定义 n+1——观测次数； S i ——第i 个时间区间结束时变量的价格，i =0，1，…n ； τ——时间区间的长度，以年为单位。 令 1ln ,0,1, ,;i i i S u i n S -?? == ??? 1.2.1 u i 的标准差s 通常估计为 s = 1.2.2 或 s = 1.2.3 其中u ?为i u 的均值。 由于i u 的标准差为。因此， 变量s 是的估计值。所以σ本身可以被估计σ∧ ，其中 σ∧ = 可以证明以上估计式的标准差大约为σ∧ 。 在计算中选择一个合适的n 值并不很容易。一般来讲，数据越多，估计的精确度也会越高，但σ确实随时间变化，因此过老的历史数据对于预测将来波动率可能不太相干。一个折中的方法是采用最近90～180天内每天的收盘价数据。另外一种约定俗成成俗的方法是将n 设定为波动率所用于的天数。因此，如果波动率是用于计算量年期的期权，在计算中我们可以采用最近两年的日收益数据。关于估计波动率表较复杂的方法涉及GARCH 模型与EWMA 模型，在下文中将进行详细介绍。 1.3.隐含波动率 首先对于一个无股息股票上看涨期权与看跌期权，它们在时间0时价格的布莱克-斯科尔斯公式为 012()()rT c S N d Ke N d -=- 1.3.1 201()()rT p Ke N d S N d -=--- 1.3.2 式中 21d =
蔬菜主要病虫害种类 十字花科蔬菜病虫：主要有小菜蛾、菜青虫、黄曲条跳甲、斜纹夜蛾、甜菜夜蛾、蚜虫、菜螟、软腐病、黑腐病、菌核病、霜霉病、炭疽病、白锈病等。 豆科蔬菜病虫：主要有豆荚螟、豆蚜、斜纹夜蛾、豆芫菁、斑潜蝇、烟粉虱、朱砂叶螨、白粉病、炭疽病、枯萎病、锈病、轮纹病、病毒病等。 葫芦科蔬菜病虫：主要有黄守瓜、芫菁、瓜娟螟、蚜虫、蓟马、斑潜蝇、烟粉虱、灰霉病、疫病、霜霉病、白粉病、炭疽病、黑斑病、叶斑病、蔓枯病、枯萎病、细菌性角斑病、病毒病等。 茄科蔬菜病虫：主要有蓟马、棉铃虫、二十八星瓢虫、茶黄螨、青枯病、早疫病、晚疫病、褐纹病、炭疽病、白粉病、灰霉病、根结线虫病等。 葱蒜类蔬菜病虫：主要有蓟马、潜叶蝇、斜纹夜蛾、甜菜夜蛾、灰霉病、疫病、霜霉病等。 蔬菜主要病虫害种类及适用农药品种 乐斯本基立甲霜灵百菌清农药杂谈分类：农业 病虫种类： 1、苗期病害（猝倒病、立枯病）发生作物：叶菜类、瓜类、茄果类苗期。适用农药：恶习霉灵、甲基立枯磷、苗菌净、多氧霉素、甲霜灵锰锌。 2、霜霉病类发生作物：瓜类、葱类、叶菜类。适用农药：甲霜灵锰锌、百菌清、氰霜唑、杀毒矾、克露、氟吗锰锌、疫霜灵。 3、疫病类型发生作物：瓜类、葱类、叶菜类。适用农药：甲霜灵锰锌、百菌清、氰霜唑、杀毒矾、克露、氟吗锰锌、疫霜灵。 4、早疫病类发生作物：辣（甜）椒、黄瓜、葱、芋等。适用农药：代森锰锌、可杀得、百菌清、甲霜灵锰锌、杀毒矾、异菌。 5、晚疫病类发生作物：番茄、马铃薯。适用农药：甲霜灵锰锌、氟吗锰锌、氰霜唑、金雷多米尔、克露、蓝保、可杀得。 6、炭疽病类发生作物：甜（辣）椒、白菜、黄瓜、菜豆。适用农药：炭特
瓜类蔬菜主要病虫害防治技术 夏季高温高湿有利于多种瓜类蔬菜病虫的发生，加上春种蔬菜收获后田间遗留大量病虫源，使防治工作更加困难。因此，夏季种植瓜类蔬菜应注意采取以下病虫害防治措施： 一、春种蔬菜收获后彻底清除田间残枝败叶，铲除田边杂草，搞好田园清洁。抓住时机赶晴天翻土晒透，以杀灭部分病菌，减少田间病虫源。 二、注意选用前茬非瓜类的农田，避免瓜类连作。育苗前要进行苗床土壤消毒，可有效防治瓜类蔬菜苗期病害。方法有：①每平方米可用1.0-1.5克绿亨一号（≥99%噁霉灵可溶性粉剂）和4克绿亨2号（80%多·福·锌可湿性粉剂），与细土20千克充分掺匀后取1/3撒在畦面，余下2/3播种后做盖土；②用绿亨8号（15%噁霉灵水剂）稀释800-1000倍苗床喷洒或淋施；③用绿亨4号（3％甲霜· 噁霉灵水剂）12-18毫升/平方米，300-500倍液喷雾或淋湿；④用绿亨一号3000-4000倍液苗床淋湿。 三、种植规格不宜过密，比春种稍疏。上棚后注意引蔓整形，保证枝叶间通透性。施肥要注意合理配方，防止偏施过量氮肥，适当提高磷钾比例，并适时补充叶面喷施微肥，如绿亨天宝等，以提高植株抗病力。 四、夏季瓜类主要病害有：霜霉病、疫病、白粉病、炭疽病、叶斑病、枯萎病、蔓枯病等。要注意使用防病杀菌剂喷洒，一般每10天左右喷一次，针对不同病害，选择相应杀菌剂。 1、疫病和霜霉病： 疫病和霜霉病是瓜类生产上的重要病害，在瓜类各生长期均可发生。其有效防治药剂有绿亨80%烯酰吗啉水分散粒剂1500-2000倍液、绿亨铜师傅86.2%氧化亚铜1500倍液，绿亨飓风70%烯酰·嘧菌酯1500倍液、72%霜脲·锰锌可湿性粉剂500-1000倍液、58%甲霜· 锰锌可湿性粉剂600-800倍液等。交替轮换使用上述药剂，可延缓病菌抗药性的产生，取得更好的防治效果。在喷施上述药剂的同时，加入绿亨天宝可在防治蔬菜病害的同时增强瓜类自身的免疫力，提高品质，增加产量。 2、白粉病： 瓜类白粉病分布广泛，全球及我国南北菜区均有发生，是危害瓜类生产的重要病害之一，黄瓜、甜瓜、南瓜、西葫芦等发生较重。可选择以下药剂进行喷雾防治：绿亨8%氟硅唑微乳剂1000-1500倍液、绿亨80%戊唑醇3000-4000倍液、250克/升苯醚甲环唑乳油3000-5000倍液，严重时配合绿亨铜师傅86.2%氧化亚铜1500倍液可解除病害抗性，通知可防治霜霉和多数细菌性病害。 3、枯萎病：
蔬菜主要病虫害综合防治技术 随着农村种植业结构的不断调整，蔬菜生产已成为我县农业的重要产业之一。蔬菜的产量、产值、经济效益均居其他农作物之首，是我县广大蔬菜种植户的主要经济来源。 近年来，我县蔬菜面积迅速扩大，品种日益增多，栽培方式多样，复种指数提高，特别是大量种植保护地和反季节蔬菜，使得多数蔬菜得以周年生产，很大程度上打破了原有蔬菜作物的生态系统的平衡，加之其它经济作物、观赏植物面积的不断增加，为多种害虫发生提供了丰富的食料条件，常规性病虫频频暴发，突发性病虫威胁增大，病虫发生种类逐步增多，几乎任何一种蔬菜都可遭受病虫害的侵染。另外，由于蔬菜种类增加、保护地面积扩大、栽培方式多样、反季节栽培及调运频繁等缘故，造成原来一些次要的病虫害逐渐上升为主要病虫害；还有因长期、过量、单一使用农药，导致病虫抗药性越来越强，防治难度越来越大。据统计,我县常年蔬菜病虫发生面积80多万亩次，防治面积90多万亩次，通过防治挽回损失1.5万吨以上。 我县蔬菜病虫害发生特点 据调查,我县发生的蔬菜病虫害有200余种,其中常年发生的虫害有5O多种,病害有70多种。在日光温室大棚蔬菜病虫中,病害重于虫害,冬春季节的霜霉病、灰霉病偏重发生，其它病虫较轻。在露地菜病虫中,虫害重于病害,病虫发生季节性明显,冬春病害重,夏季和春夏之交病虫并重,秋季虫害重。全年病害发生高峰为苗期的立枯病、猝倒病和6月份的疫病、枯萎病等，特别是辣椒、豇豆、黄瓜的疫病和瓜类的枯萎病等。全年虫害发生高峰在7～9月份，主要是小菜蛾、斜纹夜蛾、甜菜夜蛾、蚜虫、美洲斑潜蝇。 当前蔬菜病虫防治中存在的主要问题 一、对病虫害缺乏了解：目前，蔬菜病虫害种类较多，尤其是保护地蔬菜，在高温高湿条件下，病虫害发生危害严重，而不少菜农不能正确识别病虫害，缺乏植物保护基本知识和病虫害综合防治技能，不懂得贯彻“预防为主，综合防治”的植保方针，依赖、误用和不合理使用化学农药的情况较为普遍。在病虫害暴发时"病急乱投医"，不能做到对症下药，经常会出现打“保险药”、打“马前炮药”、“马后炮药”、“乱打药”、打“高毒剧毒药”及“打错药”或不严格控制农药安全间隔期等现象。长期使用单一种农药:有的菜农发现某种农药效果好，就长期使用，即使发现该药对病虫害的防治效果下降，也不换用其它农药产品，而是采取加大药量的方法，结果随着用药量的增加，病虫害的抗药性也不断增强。如某菜农在防治番茄病毒病时，长期使用20%病毒A，而不更换其它的农药，导致防治病毒病效果降低。 二、缺乏无公害生产关键技术：蔬菜病虫害的防治，长期以来依赖于化学农药防治，其弊端越来