粒子群优化小波神经网络时间序列预测，粒子群优化小波神经网络回归分析，PSO-ANN时间序列预测

%读取数据 clc clear close all clear global ver %训练数据和预测数据 num1 = xlsread('原始数据.xlsx'); num = []; for ii = 1:length(num1)-5 num = [num;num1(ii:ii+5)']; end input = num(1:336,1:5); output = num(1:336,6); input_train=num((1:336),1:5)'; output_train=num((1:336),6)'; input_test=num((337:415),1:5)'; output_test=num((337:415),6)'; [wavennoutput1,error1,cWij,cWjk,ca,cb,error2] = wavenn(num); [AllSamInn,minAllSamIn,maxAllSamIn,AllSamOutn,minAllSamOut,maxAllSamOut]=premnmx(input_train,output_train); EvaSamIn=input_test; EvaSamInn=tramnmx(EvaSamIn,minAllSamIn,maxAllSamIn); % preprocessing output_test1=tramnmx(output_test,minAllSamOut,maxAllSamOut); % preprocessing Ptrain = AllSamInn; Ttrain = AllSamOutn; indim=5; hiddennum=8; outdim=1; % Initialize PSO vmax=0.151; % 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+3)*hiddennum;%+(hiddennum+1)*outdim; gbests = [reshape(cWij,1,8) reshape(cWjk,1,40) reshape(ca,1,8) reshape(cb,1,8)] ; % 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 X(1,:,1) = gbests; %Initialize velocities of particles V=0.2*(m+(n-m)*rand(N,D,1)); % % global fvrec; MinFit=[]; BestFit=[]; fitness=fitcal(X,indim,hiddennum,outdim,D,Ptrain',Ttrain,minAllSamOut,maxAllSamOut,EvaSamInn,output_test1);%,cWij,cWjk,ca,cb 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)) fitness=fitcal(X,indim,hiddennum,outdim,D,Ptrain',Ttrain,minAllSamOut,maxAllSamOut,EvaSamInn,output_test1); [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,:)); M1 = 5; n1 = 8; N1 =1; Wij=gbest(1,1:n1,jj); Wjk=reshape(gbest(1,(hiddennum+1):(indim*hiddennum+hiddennum),jj),8,5); a=gbest(1,((indim+1)*hiddennum+1):((indim+2)*hiddennum),jj); b=gbest(1,((indim+2)*hiddennum+1):((indim+3)*hiddennum),jj); %网络预测 x = EvaSamInn'; y=zeros(1,N1); net=zeros(1,n1); net_ab=zeros(1,n1); [wavennoutput2,error,error3] = wavenn1(num,Wij,Wjk,a,b); output_test = (output_test); error=wavennoutput2-output_test; errormape=(wavennoutput2-output_test)./output_test; p1 = sum(abs(error))/115; MSE= mse(error) figure(1) grid hold on plot((BestFit),'r'); title('粒子群算法优化小波神经网络 '); xlabel('进化代数');ylabel('误差'); disp('适应度变量'); figure(2) grid plot(wavennoutput2,'-^g') hold on plot(wavennoutput1,'-ob') hold on plot(output_test,'-*r'); legend('粒子群优化小波神经网络预测输出','小波神经网络预测输出','期望输出') title('粒子群优化小波神经网络预测输出','fontsize',12) ylabel('函数输出','fontsize',12) xlabel('样本','fontsize',12) % % % mapb = sum(abs(error1./output_test))/115 % mapp = sum(abs(error./output_test))/115 figure(3) plot(error1./output_test,'*g-'); hold on plot((wavennoutput2-output_test)./output_test,'*r-') hold off title('粒子群优化小波神经网络预测误差百分比') legend('小波神经预测输出','粒子群优化小波神经网络预测输出') % save maynet1.mat wavennoutput1 wavennoutput2 % % %% figure(10) grid plot(wavennoutput2,':og') hold on plot(output_test,'-*r'); hold off legend('粒子群优化小波神经网络预测输出','期望输出') title('粒子群优化小波神经网络预测输出','fontsize',12) % ylabel('函数输出','fontsize',12) xlabel('样本','fontsize',12) figure(5) plot(wavennoutput1,':og') hold on plot(output_test,'-*'); legend('小波神经预测输出','期望输出') title('小波神经网络','fontsize',12) xlabel('样本','fontsize',12) %预测误差 error1=wavennoutput1-output_test; junfanggen = mse(wavennoutput1-output_test); figure(6) plot(error1,'-k*') title('小波神经预测误差','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('粒子群优化小波神经网络预测误差') % axis([1 2500 -0.2 0.2]) figure plot(error2,'k-') hold on plot(error3,'r-') hold off legend('小波神经预测输出','粒子群优化小波神经网络预测输出') % xlabel('') % ylabel('')