粒子群优化灰色神经网络，pso-gnn代码，MATLB编程，可以运行，包括数据

function [EvaSamOut,net] = psobp(ERR,num) n1 = randperm(19); n2 = [7 8 15 20 23]-6; input_train=num(n1,2:17)'; output_train=ERR(n1,1)'; input_test=num(n2,2:17)'; output_test=ERR(n2,1)'; [BPoutput1,error1,net] = bpp(ERR,num); [AllSamInn,minAllSamIn,maxAllSamIn,AllSamOutn,minAllSamOut,maxAllSamOut]=premnmx(input_train,output_train); EvaSamIn=input_test; EvaSamInn=tramnmx(EvaSamIn,minAllSamIn,maxAllSamIn); % preprocessing Ptrain = AllSamInn; Ttrain = AllSamOutn; indim=16; hiddennum=20; outdim=1; % Initialize PSO vmax=0.0151; % Maximum velocity minerr=0.001; % Minimum error wmax=0.90; wmin=0.30; % global itmax; %Maximum iteration number itmax=100; c1=2; c2=2; for iter=1:itmax W(iter)=wmax-((wmax-wmin)/itmax)*iter; % weight declining linearly end %Between (m,n), (which can also be started from zero) m=-1; n=1; % global N; % number of particles N=20; % global D; % length of particle D=(indim+1)*hiddennum+(hiddennum+1)*outdim; gbests = [reshape(net.IW{1,1},1,320) reshape(net.LW{2,1},1,20) reshape(net.b{1},1,20) reshape(net.b{2},1,1)] ; % particles are initialized between (a,b) randomly a=abs(gbests)*0.5+gbests; b=-abs(gbests)*0.5+gbests; % Initialize positions of particles % rand('state',sum(100*clock)); X = []; for ii = 1:N X =[X;a+(b-a).*rand(1,D,1)]; %取值范围[-1,1] rand * 2 - 1 ,rand 产生[0,1]之间的随机数 end %Initialize velocities of particles V=0.2*(m+(n-m)*rand(N,D,1)); % % global fvrec; MinFit=[]; BestFit=[]; net=newff(minmax(Ptrain),[hiddennum,outdim],{'tansig','tansig'},'traingdx'); 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,:); end for ii=1:N; pbest(ii,:,1)=X(ii,:); end V(:,:,2)=W(1)*V(:,:,1)+c1*rand*(pbest(:,:,1)-X(:,:,1))+c2*rand*(G(:,:,1)-X(:,:,1)); 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 ni=1:N for di=1:D if X(ni,di,2)>1 X(ni,di,2)=1; elseif X(ni,di,2)<-1 X(ni,di,2)=-1; else X(ni,di,2)=X(ni,di,2); end end end %****************************************************** for jj=2:itmax disp('Iteration and Current Best Fitness') disp(jj-1) disp(B(1,1,jj-1)) reset =1; % reset = 1时设置为粒子群过分收敛时将其打散，如果＝1则不打散 if reset==1 bit = 1; for k=1:N bit = bit&(range(X(k,:))<0.02); end if bit==1 % bit=1时对粒子位置及速度进行随机重置 for ik = 1:N X(ik,:) = funx; % present 当前位置,随机初始化 X(ik,:) = [0.02*rand()-0.01 0.02*rand()-0.01]; % 速度初始化 end end end % Calculation of new positions fitness=fitcal(X,net,indim,hiddennum,outdim,D,Ptrain,Ttrain,minAllSamOut,maxAllSamOut); [C,I]=min(fitness(:,1,jj)); MinFit=[MinFit C]; BestFit=[BestFit min(MinFit)]; L(:,1,jj)=fitness(:,1,jj); B(1,1,jj)=C; gbest(1,:,jj)=X(I,:,jj); [C,I]=min(B(1,1,:)); % keep gbest is the best particle of all have occured if B(1,1,jj)<=C gbest(1,:,jj)=gbest(1,:,jj); else gbest(1,:,jj)=gbest(1,:,I); end if C<=minerr break end %Matrix composed of gbest vector if jj>=itmax break end for p=1:N G(p,:,jj)=gbest(1,:,jj); end for ii=1:N; [C,I]=min(L(ii,1,:)); if L(ii,1,jj)<=C pbest(ii,:,jj)=X(ii,:,jj); else pbest(ii,:,jj)=X(ii,:,I); end end V(:,:,jj+1)=W(jj)*V(:,:,jj)+c1*rand*(pbest(:,:,jj)-X(:,:,jj))+c2*rand*(G(:,:,jj)-X(:,:,jj)); for ni=1:N for di=1:D if V(ni,di,jj+1)>vmax V(ni,di,jj+1)=vmax; elseif V(ni,di,jj+1)<-vmax V(ni,di,jj+1)=-vmax; else V(ni,di,jj+1)=V(ni,di,jj+1); end end end X(:,:,jj+1)=X(:,:,jj)+V(:,:,jj+1); for ni=1:N for di=1:D if X(ni,di,jj+1)>1 X(ni,di,jj+1)=1; elseif X(ni,di,jj+1)<-1 X(ni,di,jj+1)=-1; else X(ni,di,jj+1)=X(ni,di,jj+1); end end end end disp('Iteration and Current Best Fitness') disp(jj) disp(B(1,1,jj)) disp('Global Best Fitness and Occurred Iteration') [C,I]=min(B(1,1,:)); % simulation network 网络拟合 for t=1:hiddennum x2iw(t,:)=gbest(1,((t-1)*indim+1):t*indim,jj); end for r=1:outdim x2lw(r,:)=gbest(1,(indim*hiddennum+1):(indim*hiddennum+hiddennum),jj); end x2b=gbest(1,((indim+1)*hiddennum+1):D,jj); x2b1=x2b(1:hiddennum).'; x2b2=x2b(hiddennum+1:hiddennum+outdim).'; net=newff(minmax(AllSamInn),[20,1],{'logsig','tansig'},'trainlm'); net.IW{1,1}=x2iw; net.LW{2,1}=x2lw; net.b{1}=x2b1; net.b{2}=x2b2; %% BP网络训练 %网络进化参数 net.trainParam.epochs=5000; net.trainParam.lr=0.1; net.trainParam.goal=0.01; % net.trainParam.show=100; % net.trainParam.showWindow=1; tic %网络训练 net=train(net,AllSamInn,AllSamOutn); toc EvaSamOutn = sim(net,EvaSamInn); EvaSamOut = postmnmx(EvaSamOutn,minAllSamOut,maxAllSamOut);%反归一化 error=EvaSamOut-output_test; errormape=(EvaSamOut-output_test)./output_test; p1 = sum(abs(error))/133; MSE= mse(error) figure(1) grid hold on plot((BestFit),'r'); title(['粒子群算法优化bp ' '最优代数＝' I]); xlabel('进化代数');ylabel('误差'); disp('适应度变量'); % figure(2) % grid % plot(EvaSamOut,'-^g') % hold on % plot(BPoutput1,'-ob') % hold on % plot(output_test,'-*r'); % % legend('粒子群优化BP预测输出','BP预测输出','期望输出') % title('粒子群优化BP网络预测输出','fontsize',12) % ylabel('函数输出','fontsize',12) % xlabel('样本','fontsize',12) figure(4) grid plot(EvaSamOut,':og') hold on hold on plot(output_test,'-*r'); legend('粒子群优化BP预测输出','真实误差') title('粒子群优化BP网络预测输出','fontsize',12) ylabel('灰色预测的真实误差','fontsize',12) xlabel('样本','fontsize',12) % % % figure(5) % % plot(BPoutput1,':og') % % hold on % % plot(output_test,'-*'); % % legend('预测输出','期望输出') % % title('·BP神经网络','fontsize',12) % % ylabel('函数输出','fontsize',12) % % xlabel('样本','fontsize',12) % %预测误差 % error1=BPoutput1-output_test; % junfanggen = mse(BPoutput1-output_test); % % figure(6) % plot(error1,'-k*') % title('BP网络预测误差','fontsize',12) % ylabel('误差','fontsize',12) % xlabel('样本','fontsize',12) % %axis([1 2500 -0.5 0.5]) % figure(7) % plot(error,'ok-'); % title('粒子群优化神经网络预测误差') % % axis([1 2500 -0.2 0.2]) % % figure(3) % plot(error1./output_test,'*g-'); % hold on % plot((EvaSamOut-output_test)./output_test/2,'*r-') % hold off % title('粒子群优化BP神经网络预测误差百分比') % legend('BP预测输出','粒子群优化BP预测输出') % figure(4) % hist(errormape); % title('PSO-BP神经网络预测误差频率分布直方图'); % ylabel('频率（次）','fontsize',12) % xlabel('相对误差','fontsize',12) % MAE=(sum(abs(errormape)))/24 %绝对平均误差 % RMSE=sqrt((sum(errormape.^2))/24)%RMSE 均方根误差公式 end