tic
clear;
clc;
load data.mat
inputnum=2;
hiddennum=5;
outputnum=1;
% input_train=input(1:1500,:)';
% input_test=input(1501:2000,:)';
% output_train=output(1:1500)';
% output_test=output(1501:2000)';
input_train=input(1:600,:)';
input_test=input(601:623,:)';
output_train=output(1:600)';
output_test=output(601:623)';
[inputn,inputps]=mapminmax(input_train);
[outputn,outputps]=mapminmax(output_train);
net=newff(inputn,outputn,hiddennum,{'tansig','purelin'},'trainlm'); %%{'tansig','purelin'}为默认的激活函数（没记错的话，有兴趣的话可以试着进行调整，trainlm为默认的训练算法，Levenberg-Marquart算法)
%% 遗传算法参数初始化
maxgen=10; %进化代数，即迭代次数
sizepop=300; %种群规模
pcross=0.3; %交叉概率选择，0和1之间
pmutation=0.1; %变异概率选择，0和1之间
numsum=inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum;
lenchrom=ones(1,numsum);
bound=[-3*ones(numsum,1) 3*ones(numsum,1)]; %数据范围
individuals=struct('fitness',zeros(1,sizepop), 'chrom',[]); %将种群信息定义为一个结构体
avgfitness=[]; %每一代种群的平均适应度
bestfitness=[]; %每一代种群的最佳适应度
bestchrom=[]; %适应度最好的染色体
for i=1:sizepop %随机产生一个种群
individuals.chrom(i,:)=Code(lenchrom,bound); %编码
x=individuals.chrom(i,:); %计算适应度
individuals.fitness(i)=fun(x,inputnum,hiddennum,outputnum,net,inputn,outputn); %染色体的适应度
end
[bestfitness bestindex]=min(individuals.fitness);
bestchrom=individuals.chrom(bestindex,:); %最好的染色体
avgfitness=sum(individuals.fitness)/sizepop; %染色体的平均适应度
trace=[avgfitness bestfitness]; % 记录每一代进化中最好的适应度和平均适应度
for num=1:maxgen
% 选择
individuals=select(individuals,sizepop);
avgfitness=sum(individuals.fitness)/sizepop;
%交叉
individuals.chrom=Cross(pcross,lenchrom,individuals,sizepop,bound);
% 变异
individuals.chrom=Mutation(pmutation,lenchrom,individuals,sizepop,num,maxgen,bound);
% 计算适应度
for j=1:sizepop
x=individuals.chrom(j,:); %个体
individuals.fitness(j)=fun(x,inputnum,hiddennum,outputnum,net,inputn,outputn);
end
%找到最小和最大适应度的染色体及它们在种群中的位置
[newbestfitness,newbestindex]=min(individuals.fitness);
[worestfitness,worestindex]=max(individuals.fitness);
% 代替上一次进化中最好的染色体
if bestfitness>newbestfitness
bestfitness=newbestfitness;
bestchrom=individuals.chrom(newbestindex,:);
end
individuals.chrom(worestindex,:)=bestchrom;
individuals.fitness(worestindex)=bestfitness;
avgfitness=sum(individuals.fitness)/sizepop;
trace=[trace;avgfitness bestfitness]; %记录每一代进化中最好的适应度和平均适应度
end
figure(1)
[r,c]=size(trace);
plot([1:r]',trace(:,2),'b--');
title(['适应度曲线 ' '终止代数＝' num2str(maxgen)]);
xlabel('进化代数');ylabel('适应度');
legend('平均适应度','最佳适应度');
disp('适应度 变量');
%% 把最优初始阀值权值赋予网络预测
% %用遗传算法优化的BP网络进行值预测
x=bestchrom;
w1=x(1:inputnum*hiddennum);
B1=x(inputnum*hiddennum+1:inputnum*hiddennum+hiddennum);
w2=x(inputnum*hiddennum+hiddennum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum);
B2=x(inputnum*hiddennum+hiddennum+hiddennum*outputnum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum);
net.iw{1,1}=reshape(w1,hiddennum,inputnum);
net.lw{2,1}=reshape(w2,outputnum,hiddennum);
net.b{1}=reshape(B1,hiddennum,1);
net.b{2}=reshape(B2,outputnum,1);
%% BP网络训练
%网络参数
net.trainParam.epochs=100;
net.trainParam.lr=0.1;
net.trainParam.goal=0.00001;
net.divideParam.trainRatio = 75/100; %默认训练集占比
net.divideParam.valRatio = 15/100; %默认验证集占比
net.divideParam.testRatio = 15/100; %默认测试集占比
%网络训练
[net,per2]=train(net,inputn,outputn);
%% BP网络预测
%数据归一化
inputn_test=mapminmax('apply',input_test,inputps);
an=sim(net,inputn_test);
test_simu=mapminmax('reverse',an,outputps);
error=test_simu-output_test;
figure(2)
plot(test_simu,':og','LineWidth',1.5)
hold on
plot(output_test,'-*','LineWidth',1.5);
legend('预测输出','期望输出')
grid on
set(gca,'linewidth',1.0);
xlabel('X 样本','FontSize',15);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ylabel('Y 输出','FontSize',15);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set(gcf,'color','w')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
title('GA-BP Network','Color','k','FontSize',15);
toc
