基于粒子群算法优化BP神经网络的时间序列回归分析，基于粒子群算法优化BP神经网络权值阈值时间序列回归分析

%读取数据 clc clear close all clear global ver %训练数据和预测数据 %训练数据和预测数据 num2 = xlsread('训练集.xlsx'); num1 = reshape(num2,24,20); num1 = num1'; num4 = xlsread('预测真实值.xlsx'); for ii = 1:18 num(ii,1:48) = [num1(ii,:) num1(ii+1,:)] ; num(ii,49:72) =num1(2+ii,:); end num(19,1:48) = [num1(19,:) num1(19+1,:)]; num(19,49:72) = num4'; input_train=num((1:19),1:48)';%训练数据的输入数据 output_train=num((1:19),49:72)';%训练数据的输出数据 input_test=num(19,1:48)';%测试数据的输入数据 output_test=num(19,49:72)'; %测试数据的输出数据 % global minAllSamOut; % global maxAllSamOut; [AllSamInn,minAllSamIn,maxAllSamIn,AllSamOutn,minAllSamOut,maxAllSamOut]=premnmx(input_train,output_train); % Evaluating Sample EvaSamIn=input_test; EvaSamInn=tramnmx(EvaSamIn,minAllSamIn,maxAllSamIn); % preprocessing % TargetOfTestSam=output_test; % add reall output of testing samples % global Ptrain; Ptrain = AllSamInn; % global Ttrain; Ttrain = AllSamOutn; % Ptest = input_train(:,350:360); % Ttest = output_train(:,350:360); % Initialize BPN parameters % global indim; indim=48; % global hiddennum; hiddennum=50; % global outdim; outdim=24; % Initialize PSO parameters 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 % 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=20; % 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); %取值范围[-1,1] rand * 2 - 1 ,rand 产生[0,1]之间的随机数 %Initialize velocities of particles V=0.2*(m+(n-m)*rand(N,D,1)); % % global fvrec; MinFit=[]; BestFit=[]; %Function to be minimized, performance function,i.e.,mse of net work 神经网络建立 % 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,:); end for i=1:N; pbest(i,:,1)=X(i,:); 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 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 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); for ni=1:N for di=1:D if X(ni,di,j+1)>1 X(ni,di,j+1)=1; elseif X(ni,di,j+1)<-1 X(ni,di,j+1)=-1; else X(ni,di,j+1)=X(ni,di,j+1); end end end 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 网络拟合 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; [BPoutput1,error1] = bpp(num); %% BP网络训练 %网络进化参数 net.trainParam.epochs=1; net.trainParam.lr=0.01; net.trainParam.goal=0.0000001; net.trainParam.show=100; net.trainParam.showWindow=1; tic %网络训练 %[net,per2]=train(net,AllSamInn,AllSamOutn); net=train(net,AllSamInn,AllSamOutn); % nettesterr=mse(sim(net,Ptest)-Ttest); % testsamout = postmnmx(sim(net,Ptest),minAllSamOut,maxAllSamOut); % realtesterr=mse(testsamout-TargetOfTestSam) toc EvaSamOutn = sim(net,EvaSamInn); EvaSamOut = postmnmx(EvaSamOutn,minAllSamOut,maxAllSamOut);%反归一化 error=EvaSamOut-output_test; errormape=(EvaSamOut-output_test)./output_test; 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,':og') hold on hold on plot(output_test,'-*r'); axis([1 24 300 600]) legend('粒子群优化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)