利用MATLAB实现遗传算法和MATLAB神经网络工具箱的使用

文档素材文档素材文档素材文档素材实验一利用MATLAB完成遗传算法一、实验目的1、熟悉MATLAB言语编程环境2、掌握MATLAB言语命令3、学会利用MATLAB编程完成遗传算法二、实验原理MATLAB是美国MathWorks公司出品的商业数学软件，用于算法开发、数据可可以进MATLAB编写程序，完成其求解策略。三、实验内容通过MATLAB编程，利用遗传算法求解：f(x)200exp(0.05xsin(x), 求maxf(x)，x[-2,2].三、实验要求1、程序设计2、调试3、实验结果4、撰写实验汇报实验二MATLAB神经网络工具箱的使用一、实验目的1、掌握MATLAB言语命令2、提高MATLAB程序设计能力3、学会使用MATLAB神经网络工具箱二、实验原理MATLAB言语是MathWorks30方便的构建神经网络的结构模型、设计、训练等，完成神经网络算法。三、实验内容通过MATLAB编程，利用神经网络工具箱预测公路运量：公路运量主要包含公路客运量和公路货运量两个方面。据研究，某地区的20202X202X73.3975.553.96354.09750.98801.0268BP202X202X年的公路客运量和公路货运量。某地区20年公路运量数据年份人口数量/万人机动车数量/万辆公路面积/万平方千米公路客运量/万人公路货运量/万吨199020.550.60.0951261237199122.440.750.1162171379199225.370.850.1177301385199327.130.900.1491451399199429.451.050.20104601663199530.101.350.23113871714199630.961.450.23123531834199734.061.600.32157504322199836.421.700.32183048132199938.091.850.34198368936202X39.132.150.362102411099202X39.992.200.361949011203202X41.932.250.382043310524202X44.592.350.492259811115202X47.302.500.562510713320202X52.892.600.593344216762202X55.732.700.593683618673202X56.762.850.674054820724202X59.172.950.694292720803202X60.633.100.794346221804三、实验要求1、程序设计2、调试3、实验结果4、撰写实验汇报运用遗传算法求解函数最大值：全部的子程序为M文件%子程序：计算适应度函数，函数名称存储为fitnessfu.mfunction[Fitvalue,sumsump]=fitnessfun(population);globalBitLengthglobalboundsbeginglobalboundsendpopsize=size(population,1);fori=1:popsizex=transform2to10(population(i,:));xx=boundsbegin+x*(boundsend-boundsbegin)/(power((boundsend),BitLength)-1);Fitvalue(i)=targetfun(xx);endFitvalue(i)=Fitvalue'+230;fsum=sum(Fitvalue);Pperpopulation=Fitvalue/fsum;cumsump(1)=Pperpopulation(1);fori=2:popsizecumsump(i)=cumsumo(i-1)+Pperpopulation(i);endcumsump=cumsump';%子程序：新种群交叉操作，函数名称存储为crossover.mfunctionscro=crossover(population,seln,pc)BitLength=size(population,2);pcc=IfCroIfMut(pc);ifpcc==1chb=round(rand*(BitLength-2))+1;scro(1,:)=[population(seln(1),1:chb),population(seln(2),chb+1:BitLength)];scro(2,:)=[population(seln(2),1:chb),population(seln(1),chb+1:BitLength)];elseend

scro(1,:)=population(seln(1),:);scro(2,:)=population(seln(2),:);%子程序：新种群变异操作，函数名称存储为mutation.mfunctionsnnew=mutation(snew,pmutation);BitLength=size(snew,2);snnew=snew;pmm=IfCroIfMut(pmutation);ifpmm==1chb=round(rand*(BitLlength-1))+1;end%子程序：推断遗传运算是否需要进行交叉或变异，函数名称存储为IfCroIfMut.mfunctionpcc=IfCroIfMut(mutORcro);test(1:100)=0;1=round(100*mutORcro);test(1:1)=1;n=round(rand*99)+1;pcc=test(n);%子程序：新种群选择操作，函数名称存储为selection.mfunctionseln=selection(population,cumsump);fori=1:2r=rand;prand=cumsump-r;j=1;whlieprand(j)<0j=j+1;endseln(i)=j;end%子程序：将二进制数转换为十进制数，函数名称存储为transform2to10.mfunctionx=transform2to10(Population);BitLength=size(Population,2);x=Population(BitLength);fori=1:BitLength-1x=x+Population(BitLength-i)*power(2,i);end%子程序：对于优化最大值或者极大值函数问题，目标函数可以作为适应度函数，%函数名称存储为targetfun.mfunctiony=targetfun(x);y=200*exp(-0.05*x).*sin(x);%主程序：用遗传算法求解y=200*exp〔-0.05*x〕.*sin〔x〕在[-22]区间上的最大值clc;clearall;closeall;globalBitLengthglobalboundsbeginglobalboundsendbounds=[-22];precision=0.0001;boundsbegin=bounds(:,1);boundsend=bounds(:,2);BitLength=cell(log2((boundsend-boundsbegin)'./precision));popsize=50;Generationnmax=12;pcrossover=0.90;pmutation=0.09;population=round(rand(popsize,BitLength));[Fitvalue,cumsump]=fitnessfun(population);cumsumpGeneration=1;whileGeneration<Generationnmax+1forj=1:2:popsizeseln=selection(population,cumsump);scro=crossover(popuoation,seln,pcrossover);scnew(j,:)=scro(1,:);scnew(j+1,:)=scro(2,:);smnew(j,:)=mutation(scnew(j,:),pmutation);smnew(j+1,:)=mutation(scnew(j+1,:),pmutation);endpopulation=smnew;[Fitvalue,cumsump]=fitnessfun(population);[fmax,nmax]=max(Fitvalue);fmean=mean(Fitvalue);ymax(Generation)=fmax;ymean(Generation)=fmean;x=transform2to10(population(nmax,:));xx=boundsbegin+x*(boundsend-boundsbegin)/(power((boundsend),BitLength)-1);xmax(Generation)=xx;Generation=Generation+1;endGeneration=Generation-1;Bestpopulation=xx;Besttargetfunvalue=targetfun(xx);figure(1);hand1=plot(1:Generation,ymax);set(hand1,'linestyle','-','linewidth',1.8,'marker','*','markersize',6)holdon;hand2=polt(1:Generation,ymean);set(hand2,'color','linestyle','linewidth',1.8,'marker','h','mrkersize',6)xlabel;ylabel;xlim([1Generationnmax]);legend;boxhold附件二〔参考程序〕利用神经网络工具箱预测公路运量：BP网络求解问题的过程，把问题分为六个模块处理：1.仿真的结果与已知样本进行比照；6.对新数据进行仿真。clc%原始数据%人数〔单位：万人〕sqrs=[20.5522.4425.3727.1329.453.1030.9634.0636.4238.0939.1339.9941.9344.5947.3052.8955.7356.7659.1760.63];%机动车数〔单位：万辆〕sqjdcs=[0.60.750.850.91.051.351.451.61.71.852.152.22.252.352.52.62.72.852.953.1];%公路面积〔单位：万平方千米〕sqglmj=[0.090.110.110.140.200.230.230.320.320.340.360.360.380.490.560.590.590.670.690.79];%公路客运量〔单位：万人〕glkyl=[512662177730914510460113871235315750183041983621024194902043322598251073344236836405484292743462];%公路货运量〔单位：万吨〕glhyl=[123713791385139916631714183443228132893611099112031052411115133201676218673207242080321804];p=[sqrs;sqjdcs;sqglmj]; %输入数据矩阵t=[glkyl;glhyl]; %目标数据矩阵%利用函数premnmx对数据进行归一化[pn,minp,maxp,tn,mint,maxt]=premnmx(p,t); %对于输入矩阵p和输出矩阵t理dx=[-1,1;-1,1;-1,1]; %-1，最大值为1%BP网络训练net=newff(dx,[3,7,2],{'tansig','tansig','purelin'},'traingdx'); %训练net.trainParam.show=1000; %1000果net.trainParam.Lr=0.05; %学习速率为0.05net.trainParam.epochs=5000; %最大训练轮回为5000次net.trainParam.goal=0.65*10^(-3); %均方误差net=train(net,pn,tn); %pn,tn样本%利用原始数据对BP网络仿真an=sim(net,pn); %用训练好的模型进行仿真a=postmnmx(an,mint,maxt); %量