elman.m

我自己编写的基于遗传算法的elman神经网络模型．大家参考！

%遗传算法优化神经网络结构及其权系数的程序采用实数编码
clear  all
clear  all
%遗传算法优化神经网络结构及其权系数的程序采用实数编码
clear  all
close all
clc
global maxjiedian
global popsize
global chromlength
global maxpop
global ny
global nx              
global hh
[file1,path1]=uigetfile('*.*','打开数据文件');
filename1=strcat(path1,file1);
fid1=fopen(filename1,'r');
prompt={'除Y外的列数','样本总数','取其中用于拟合的样本个数','输入需要预测向量因子'};
def={'10','48','46','0 0 0 0 0 0 0 0 0 0'};
lineNo=1;
dlgTitle='参数输入，遗传算法集成神经网络，吴建生2005/09/10';
answer=inputdlg(prompt,dlgTitle,lineNo,def);
answer=char(answer);           
ny=str2num(answer(1,:));
nx=str2num(answer(2,:));
it=str2num(answer(3,:));
wxyq=str2num(answer(4,:));              
prompt={'输入节点个数','种群个数','最大进化代数','隐节点下限','隐节点上限','输出节点个数'};
def={'10','50','50','0.5','1.5','1'};
dlgTile='参数输入，   遗传算法集成神经网络的参数';
lineNo=1;
answer=inputdlg(prompt,dlgTile,lineNo,def);
answer=char(answer);
maxjiedian=str2num(answer(1,:));
popsize=str2num(answer(2,:));
maxpop=str2num(answer(3,:));
wyt=str2num(answer(4,:));
lyt=str2num(answer(5,:));
gx=str2num(answer(6,:));
prompt={'连接权下限','连接权上限','交叉概率1','交叉概率2','交叉概率3','变异概率1','变异概率2','变异概率3','变异强度'};
def={'-2.0','2.0','0.6','0.6','0.6','0.05','0.05','0.05','0.01'};
dlgTile='参数输入，   遗传算法优化神经网络权系数参数输入';
lineNo=1;
answer=inputdlg(prompt,dlgTile,lineNo,def);
answer=char(answer);
xyy1=str2num(answer(1,:));
xyy2=str2num(answer(2,:));
pc1=str2num(answer(3,:));
pc2=str2num(answer(4,:));
pc3=str2num(answer(5,:));
pm1=str2num(answer(6,:));
pm2=str2num(answer(7,:));
pm3=str2num(answer(8,:));
pm4=str2num(answer(9,:));
prompt={'最大训练的次数','目标误差','显示间隔次数','学习速率','动量因子'};
def={'200','0.01','100','0.0025','0.005'};
lineNo=1;
dlgTitle='参数输入，神经网络程序吴建生';
answer=inputdlg(prompt,dlgTitle,lineNo,def);
answer=char(answer);
cx=str2num(answer(1,:));
dx=str2num(answer(2,:));
ex=str2num(answer(3,:));
alfa=str2num(answer(4,:));
bieta=str2num(answer(5,:));               
[file2,path2]=uiputfile('*.*','请选择要保存结果的文件夹名');
filename2=strcat(path2,file2);                
fid2=fopen(filename2,'w');              
AA=fscanf(fid1,'%f');
BB=reshape(AA,ny+gx,nx)';
[px,py]=size(BB);
fprintf(fid2,'****############*********遗传算法集成神经网络权系数和网络结构的结果*********#############****\n');
fprintf(fid2,'******#######################原始数据得结果距阵#######################################******\n');
for i=1:nx
    for j=1:ny+gx
        fprintf(fid2,'%12.5f',BB(i,j));
    end
    fprintf(fid2,'\n');
end
sw1w=it;
for TGB=1:(nx-sw1w);
    BB1=BB(1:it,1:ny);
    BB2=BB(1:it,ny+1:ny+gx);
    BB3=BB(it+1,1:ny);
    BB4=BB(it+1,ny+1:ny+gx);
    BB5=wxyq;
    Y1=BB2;
    Y2=BB4;
    Y3=[Y1;Y2];
    [pn,minp,maxp,tn,mint,maxt]=premnmx(BB1',BB2');
  
    p1=pn;
    t1=tn;
    p2=tramnmx(BB3',minp,maxp);
    t2=tramnmx(BB4',mint,maxt);
    p3=tramnmx(BB5',minp,maxp);
    p4=[p1 p2];
    t3=Y1';
    t4=Y2';
    t5=[t1 t2];
    generation=1;
    %编码的总串长度chromlegntg=stepwise*(maxjiedian+2)
    format long
    yty=floor(maxjiedian*lyt);
    chromlength=(yty*(ny+2)+1);
    rand('seed',78341223);
    pop=UNIFRND(xyy1,xyy2,popsize,chromlength);   
    sqa1=(maxjiedian.*lyt);
    sqa2=(maxjiedian.*wyt);
    rand('seed',78341223);
    you=floor((rand(popsize,1)).*(sqa1-sqa2)+sqa2);
    maxn=max(you);
    chromlength2=(maxn*maxn);
    popo=UNIFRND(xyy1,xyy2,popsize,chromlength2);
    p1_=zeros(chromlength2,chromlength2); 
    
    %解码过程
    hh=ny*yty;
    for i=1:popsize 
        n=you(i,1);%隐节点个数
        c_2=popo(i,1:n*n);
        p1_2=p1_(i,1:n)';
        c=pop(i,1:n*ny);%从输入层到隐层的权值
        d=pop(i,hh+1:hh+n)';%隐层的阈限
        e=pop(i,hh+yty+1:hh+yty+n);%从隐层到输出层的权值
        g=pop(i,chromlength);%输出层的阈限
        f=reshape(c,n,ny);%从输入层到隐层的权值作成n*ny的矩阵
        f_2=reshape(c_2,n,n);
        for j=1:it 
            aatq=f*p1(:,j);        %训练样本与从输入层到隐层的权值结合
            aatq_2=f_2*p1_2; 
            fert=aatq+d+aatq_2;
            fet=1./(1+exp(-fert));
            %记忆==
            p1_2=fet+(p1_2.*0.5);%将第m个样本的隐层输出作为连接层的第m+1个样本
            %==== 
            rewqt=e*fet;
            dmf=rewqt+g;                
            dmn(i,j)=postmnmx(dmf,mint,maxt);
        end
        obj(i)=1/(1+mean(abs(dmn(i,:)-t3)));         
    end  
    objval=obj';
    [maxfit,bestindex]=max(objval);
    maxfitting(generation,1)=maxfit;
    hyu1=pop(bestindex,:);
    hyu1o=popo(bestindex,:);
    bestpop(generation,:)=hyu1;
    bestpopo(generation,:)=hyu1o;
    hyu2=you(bestindex,:)
    bestyou(generation,:)=hyu2;  
    [mifit,worstindex]=min(objval);
    mifitting(generation,1)=mifit;
    hyu3=pop(worstindex,:);
    hyu3o=popo(worstindex,:);
    worstpop(generation,:)=hyu3;
    worstpopo(generation,:)=hyu3o;
    hyu4=you(worstindex,:);
    worstyou(generation,:)=hyu4;  
    fitvalue=objval;
    fitvalue1=fitvalue/sum(fitvalue);             
    cfitness=cumsum(fitvalue1);
    currentbest=bestindex;
    
    bar=waitbar(0,'please wait....');
    while (generation<maxpop)
        waitbar(generation/maxpop,bar);
        generation=generation+1;
        [px11,py11]=size(cfitness);                      
        rans=sort(rand(px11,1));   
        fitin=1;
        newin=1;
        lx=hh;
        ly=(hh+yty);
        lt=(hh+2*yty);
        pop1=pop(:,1:lx);
        pop2=pop(:,lx+1:ly);
        pop3=pop(:,ly+1:lt);
        pop4=pop(:,lt+1);                  
        fitind=1;
        newind=1;
        %复制最佳个体
        while newind<=px11
            if(rans(newind))<cfitness(fitind)
                newpop1(newind,:)=pop1(fitind,:);
                newpop2(newind,:)=pop2(fitind,:);
                newpop3(newind,:)=pop3(fitind,:);
                newpop4(newind,:)=pop4(fitind,:);
                newyou(newind,:)=you(fitind,:);
                newpopo(newind,:)=popo(fitind,:);
                newind=newind+1;
            else
                fitind=fitind+1;
            end
        end
        copyw1=newpop1;
        copyw2=newpop2;
        copyw3=newpop3;
        copyw4=newpop4;
        copyw5=newpopo;
        copyyou=newyou;
        [px3,py3]=size(copyw1);
        [px4,py4]=size(copyw2);
        [px5,py5]=size(copyw3);
        [px6,py6]=size(copyw4);
        [px7,py7]=size(copyw5);
        %rand('seed',78341223);
        c=rand(px3,py3);
        %交叉种群的第一个  
        for i=1:2:px3-1
            if(rand<pc1)
                overw1(i,:)=copyw1(i,:).*c(i,:)+copyw1(i+1,:).*(1-c(i,:));
                overw1(i+1,:)=copyw1(i,:).*(1-c(i,:))+copyw1(i+1,:).*c(i,:);
            else
                overw1(i,:)=copyw1(i,:);
                overw1(i+1,:)=copyw1(i+1,:);
            end
        end
        overpop1=overw1;
        %rand('seed',78341223);
        c1=rand(px4,py4);
        %交叉第二个种群
        for i=1:2:px4-1
            if(rand<pc2)
                overw2(i,:)=copyw2(i,:).*c1(i,:)+copyw2(i+1,:).*(1-c1(i,:));
                overw2(i+1,:)=copyw2(i,:).*(1-c1(i,:))+copyw2(i+1,:).*c1(i,:);
            else
                overw2(i,:)=copyw2(i,:);
                overw2(i+1,:)=copyw2(i+1,:);
            end
        end 
        overpop2=overw2;
        %rand('seed',78341223);
        c2=rand(px5,py5);
        %交叉第三个种群
        for i=1:2:px5-1
            if(rand<pc2)
                overw3(i,:)=copyw3(i,:).*c2(i,:)+copyw3(i+1,:).*(1-c2(i,:));
                overw3(i+1,:)=copyw3(i,:).*(1-c2(i,:))+copyw3(i+1,:).*c2(i,:);
            else
                overw3(i,:)=copyw3(i,:);
                overw3(i+1,:)=copyw3(i+1,:);
            end
        end
        overpop3=overw3;
        %rand('seed',78341223);
        c3=rand(px6,py6);
        %交叉第四个种群
        for i=1:2:px6-1
            if(rand<pc2)
                overw4(i,:)=copyw4(i,:).*c3(i,:)+copyw4(i+1,:).*(1-c3(i,:));
                overw4(i+1,:)=copyw4(i,:).*(1-c3(i,:))+copyw4(i+1,:).*c3(i,:);
            else
                overw4(i,:)=copyw4(i,:);
                overw4(i+1,:)=copyw4(i+1,:);
            end
        end
        overpop4=overw4;
        c5=rand(px7,py7);
        %交叉第四个种群
        for i=1:2:px7-1
            if(rand<pc2)
                overw5(i,:)=copyw5(i,:).*c5(i,:)+copyw5(i+1,:).*(1-c5(i,:));
                overw5(i+1,:)=copyw5(i,:).*(1-c5(i,:))+copyw5(i+1,:).*c5(i,:);
            else
                overw5(i,:)=copyw5(i,:);
                overw5(i+1,:)=copyw5(i+1,:);
            end
        end
        overpopo=overw5;
        [px99,py99]=size(copyyou);
        %rand('seed',78341223);
        c4=rand(px99,py99);
        for i=1:2:px99-1
            if(rand<pc3)
                overy(i,:)=copyyou(i,:).*c3(i,:)+copyyou(i+1,:).*(1-c3(i,:));
                overy(i+1,:)=copyyou(i,:).*(1-c3(i,:))+copyyou(i+1,:).*c3(i,:);
            else
                overy(i,:)=copyyou(i,:);
                overy(i+1,:)=copyyou(i+1,:);
            end
        end
        overyou=floor(overy);
        [px7,py7]=size(overpop1);
        [px8,py8]=size(overpop2);
        [px9,py9]=size(overpop3);
        [px10,py10]=size(overpop4);
        [px11,py11]=size(overyou);
        [px12,py12]=size(overpopo);
        
        %进行变异过程
        %rand('seed',78341223);
        cc1=rand(px7,py7);
        for i=1:px7
            if(rand<pm1)
                u1=min(overpop1(i,:));
                u2=max(overpop1(i,:));
                u3=u2-u1;
                mutionpop1(i,:)=u3.*cc1(i,:)+u1; 
            else
                mutionpop1(i,:)=overpop1(i,:);
            end
        end 
        mutpop1=mutionpop1;
        %fitst2 mution
        %rand('seed',78341223);
        cc2=rand(px8,py8);
        for i=1:px8
            if(rand<pm2)
                u1=min(overpop2(i,:));
                u2=max(overpop2(i,:));
                u3=u2-u1;
                mutionpop2(i,:)=u3.*cc2(i,:)+u1;    
            else
                mutionpop2(i,:)=overpop2(i,:);
            end
        end 
        mutpop2=mutionpop2;
        %fitst3 mution
        %rand('seed',78341223);
        cc3=rand(px9,py9);