BP.rar_PSO_PSO-BP_bp训练_pso BP MATLAB_pso-bp matlab

非常感谢很多朋友对这个东西感兴趣！ 很多朋友没有技术分，给我发了很多电子邮件。因工作原因，恕我未能一一回复！现再次上传，开放给各位朋友浏览，仅供参考！ 下面是我写的PSO训练BP网络的代码，训练样本被我抽去了。 各位下载后，适当修改即可运行。如有问题，先自行研究，实在不行再请发帖或Email交流。 function psobp % BP neural network trained by PSO algorithm % Copyright by Deng Da-Peng @ 2005 % Email: rexdeng@163.com % You can change and distribute this code freely for academic usage % Business usage is strictly prohibited clc clear all AllSamIn=...; % Add your all input data AllSamOut-...; % Add your all output data % Pre-processing data with premnmx, you can use other functions global minAllSamOut; global maxAllSamOut; [AllSamInn,minAllSamIn,maxAllSamIn,AllSamOutn,minAllSamOut,maxAllSamOut] = premnmx(AllSamIn,AllSamOut); % draw 10 percent from all samples as testing samples，the rest as training samples i=[10:10:1000]; TestSamIn=[]; TestSamOut=[]; for j=1:100 TestSamIn=[TestSamIn,AllSamInn(:,i(j))]; TestSamOut=[TestSamOut,AllSamOutn(:,i(j))]; end TargetOfTestSam=...; % add reall output of testing samples TrainSamIn=AllSamInn; TrainSamOut=AllSamOutn; TrainSamIn(:,i)=[]; TrainSamOut(:,i)=[]; % Evaluating Sample EvaSamIn=... EvaSamInn=tramnmx(EvaSamIn,minAllSamIn,maxAllSamIn); % preprocessing global Ptrain; Ptrain = TrainSamIn; global Ttrain; Ttrain = TrainSamOut; Ptest = TestSamIn; Ttest = TestSamOut; % Initialize BPN parameters global indim; indim=5; global hiddennum; hiddennum=3; global outdim; outdim=1; % Initialize PSO parameters vmax=0.5; % Maximum velocity minerr=0.001; % Minimum error wmax=0.90; wmin=0.30; global itmax; %Maximum iteration number itmax=300; c1=2; c2=2; for iter=1:itmax W(iter)=wmax-((wmax-wmin)/itmax)*iter; % weight declining linearly end % particles are initialized between (a,b) randomly a=-1; b=1; %Between (m,n), (which can also be started from zero) m=-1; n=1; global N; % number of particles N=40; global D; % length of particle D=(indim+1)*hiddennum+(hiddennum+1)*outdim; % Initialize positions of particles rand('state',sum(100*clock)); X=a+(b-a)*rand(N,D,1); %Initialize velocities of particles V=m+(n-m)*rand(N,D,1); global fvrec; MinFit=[]; BestFit=[]; %Function to be minimized, performance function,i.e.,mse of net work global net; net=newff(minmax(Ptrain),[hiddennum,outdim],{'tansig','purelin'}); fitness=fitcal(X,net,indim,hiddennum,outdim,D,Ptrain,Ttrain,minAllSamOut,maxAllSamOut); fvrec(:,1,1)=fitness(:,1,1); [C,I]=min(fitness(:,1,1)); MinFit=[MinFit C]; BestFit=[BestFit C]; L(:,1,1)=fitness(:,1,1); %record the fitness of particle of every iterations B(1,1,1)=C; %record the minimum fitness of particle gbest(1,:,1)=X(I,:,1); %the global best x in population %Matrix composed of gbest vector for p=1:N G(p,:,1)=gbest(1,:,1); end for i=1:N; pbest(i,:,1)=X(i,:,1); end V(:,:,2)=W(1)*V(:,:,1)+c1*rand*(pbest(:,:,1)-X(:,:,1))+c2*rand*(G(:,:,1)-X(:,:,1)); %V(:,:,2)=cf*(W(1)*V(:,:,1)+c1*rand*(pbest(:,:,1)-X(:,:,1))+c2*rand*(G(:,:,1)-X(:,:,1))); %V(:,:,2)=cf*(V(:,:,1)+c1*rand*(pbest(:,:,1)-X(:,:,1))+c2*rand*(G(:,:,1)-X(:,:,1))); % limits velocity of particles by vmax for ni=1:N for di=1:D if V(ni,di,2)>vmax V(ni,di,2)=vmax; elseif V(ni,di,2)<-vmax V(ni,di,2)=-vmax; else V(ni,di,2)=V(ni,di,2); end end end X(:,:,2)=X(:,:,1)+V(:,:,2); %****************************************************** for j=2:itmax disp('Iteration and Current Best Fitness') disp(j-1) disp(B(1,1,j-1)) % Calculation of new positions fitness=fitcal(X,net,indim,hiddennum,outdim,D,Ptrain,Ttrain,minAllSamOut,maxAllSamOut); fvrec(:,1,j)=fitness(:,1,j); %[maxC,maxI]=max(fitness(:,1,j)); %MaxFit=[MaxFit maxC]; %MeanFit=[MeanFit mean(fitness(:,1,j))]; [C,I]=min(fitness(:,1,j)); MinFit=[MinFit C]; BestFit=[BestFit min(MinFit)]; L(:,1,j)=fitness(:,1,j); B(1,1,j)=C; gbest(1,:,j)=X(I,:,j); [C,I]=min(B(1,1,:)); % keep gbest is the best particle of all have occured if B(1,1,j)<=C gbest(1,:,j)=gbest(1,:,j); else gbest(1,:,j)=gbest(1,:,I); end if C<=minerr, break, end %Matrix composed of gbest vector if j>=itmax, break, end for p=1:N G(p,:,j)=gbest(1,:,j); end for i=1:N; [C,I]=min(L(i,1,:)); if L(i,1,j)<=C pbest(i,:,j)=X(i,:,j); else pbest(i,:,j)=X(i,:,I); end end V(:,:,j+1)=W(j)*V(:,:,j)+c1*rand*(pbest(:,:,j)-X(:,:,j))+c2*rand*(G(:,:,j)-X(:,:,j)); %V(:,:,j+1)=cf*(W(j)*V(:,:,j)+c1*rand*(pbest(:,:,j)-X(:,:,j))+c2*rand*(G(:,:,j)-X(:,:,j))); %V(:,:,j+1)=cf*(V(:,:,j)+c1*rand*(pbest(:,:,j)-X(:,:,j))+c2*rand*(G(:,:,j)-X(:,:,j))); for ni=1:N for di=1:D if V(ni,di,j+1)>vmax V(ni,di,j+1)=vmax; elseif V(ni,di,j+1)<-vmax V(ni,di,j+1)=-vmax; else V(ni,di,j+1)=V(ni,di,j+1); end end end X(:,:,j+1)=X(:,:,j)+V(:,:,j+1); end disp('Iteration and Current Best Fitness') disp(j) disp(B(1,1,j)) disp('Global Best Fitness and Occurred Iteration') [C,I]=min(B(1,1,:)) % simulation network for t=1:hiddennum x2iw(t,:)=gbest(1,((t-1)*indim+1):t*indim,j); end for r=1:outdim x2lw(r,:)=gbest(1,(indim*hiddennum+1):(indim*hiddennum+hiddennum),j); end x2b=gbest(1,((indim+1)*hiddennum+1):D,j); x2b1=x2b(1:hiddennum).'; x2b2=x2b(hiddennum+1:hiddennum+outdim).'; net.IW{1,1}=x2iw; net.LW{2,1}=x2lw; net.b{1}=x2b1; net.b{2}=x2b2; nettesterr=mse(sim(net,Ptest)-Ttest); testsamout = postmnmx(sim(net,Ptest),minAllSamOut,maxAllSamOut); realtesterr=mse(testsamout-TargetOfTestSam) EvaSamOutn = sim(net,EvaSamInn); EvaSamOut = postmnmx(EvaSamOutn,minAllSamOut,maxAllSamOut); figure(1) grid hold on plot(log(BestFit),'r'); figure(2) grid hold on plot(EvaSamOut,'k'); save er net nettesterr realtesterr B fvrec EvaSamOut %sub function for getting fitness of all paiticles in specific generation %change particle to weight matrix of BPN,then calculate training error function fitval = fitcal(pm,net,indim,hiddennum,outdim,D,Ptrain,Ttrain,minAllSamOut,maxAllSamOut) [x,y,z]=size(pm); for i=1:x for j=1:hiddennum x2iw(j,:)=pm(i,((j-1)*indim+1):j*indim,z); end for k=1:outdim x2lw(k,:)=pm(i,(indim*hiddennum+1):(indim*hiddennum+hiddennum),z); end x2b=pm(i,((indim+1)*hiddennum+1):D,z); x2b1=x2b(1:hiddennum).'; x2b2=x2b(hiddennum+1:hiddennum+outdim).'; net.IW{1,1}=x2iw; net.LW{2,1}=x2lw; net.b{1}=x2b1; net.b{2}=x2b2; error=sim(net,Ptrain)-Ttrain; fitval(i,1,z)=mse(error); end