基于matlab采用粒子群算法PSO优化BP神经网络，进行风电功率预测，含实际数据和案例

% function psobp % BP neural network trained by PSO algorithm % Copyright by Deng Da-Peng @ 2005 % Email: [email protected] % You can change and distribute this code freely for academic usage % Business usage is strictly prohibited % 清空环境变量 close all clc clear % AllSamIn=input_train; % Add your all input data % AllSamOut=output_train; % 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 %读取数据 load traindata1011 A O load goontest inputtest_may16 outputtest_may16 %训练数据和预测数据 input_train=A(1:360,:)'; input_test=inputtest_may16(1:24,:)'; output_train=O(1:360)'; output_test=outputtest_may16(1:24)'; 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 = input_train; global Ttrain; Ttrain = output_train; % Ptest = input_train(:,350:360); % Ttest = output_train(:,350:360); % Initialize BPN parameters global indim; indim=4; global hiddennum; hiddennum=5; 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=200; 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); %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=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,:,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 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; %% BP网络训练 %网络进化参数 net.trainParam.epochs=100; net.trainParam.lr=0.1; net.trainParam.goal=0.00001; net.trainParam.show=100; net.trainParam.showWindow=0; %网络训练 [net,per2]=train(net,AllSamInn,AllSamOutn); % 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);%反归一化 error=EvaSamOut-output_test; errormape=(EvaSamOut-output_test)./output_test; figure(1) grid hold on plot(log(BestFit),'r'); title(['PSO适应度曲线 ' '最优代数＝' I]); xlabel('进化代数');ylabel('适应度'); legend('平均适应度','最佳适应度'); disp('适应度 变量'); figure(2) grid plot(EvaSamOut,':og') hold on plot(output_test,'-*'); legend('预测输出','期望输出') title('PSO-BP网络预测输出','fontsize',12) ylabel('函数输出','fontsize',12) xlabel('样本','fontsize',12) % figure(3) % plot(error1,'-*'); % title('神经网络预测误差百分比') 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