%读取数据
clc
clear
close all
warning off
clear global ver
%训练数据和预测数据
load maydata1.mat
%% 划分数据集
[mm nm]=sort(rand(1,2000));
mm=1000;
input_train=num(nm(1:mm),1:102)';
output_train=num(nm(1:mm),103)';
input_test=num((mm+1:end),1:102)';
output_test1=num((mm+1:end),103)';
[elmanoutput1,error1,net1] = funelman(num,nm);
[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=102;
hiddennum=30;
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(net1.IW{1,1},1,3060) reshape(net1.LW{2,1},1,30) reshape(net1.b{1},1,30) reshape(net1.b{2},1,1)] ;
% particles are initialized between (a,b) randomly
a=abs(gbests)*0.2+gbests;
b=-abs(gbests)*0.2+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=newelm(minmax(Ptrain),[hiddennum,outdim],{'tansig','tansig'});
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)>a(di)
X(ni,di,2)=a(di);
elseif X(ni,di,2)<b(di)
X(ni,di,2)=b(di);
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)>a(di)
X(ni,di,jj+1)=a(di);
elseif X(ni,di,jj+1)<b(di)
X(ni,di,jj+1)=b(di);
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.IW{1,1}=x2iw;
% net.LW{2,1}=x2lw;
% net.b{1}=x2b1;
% net.b{2}=x2b2;
net=newelm(Ptrain,Ttrain,[hiddennum],{'tansig','tansig'});
%% elman网络训练
%网络进化参数
net.trainParam.epochs=2000;
% net.trainParam.lr=0.1;
net.trainParam.goal=0.00001;
% net.trainParam.show=100;
% net.trainParam.showWindow=1;
net=init(net);
tic
%网络训练
net=train(net,AllSamInn,AllSamOutn);
toc
EvaSamOutn = sim(net,EvaSamInn);
EvaSamOut = (postmnmx(EvaSamOutn,minAllSamOut,maxAllSamOut));%反归一化
output_test=(output_test1);
% save maydata.mat
load maydata.mat
error=EvaSamOut-output_test;
errormape=(EvaSamOut-output_test)./output_test;
[MSE1,RMSE1,MBE1,MAE1,MAPE1] =MSE_RMSE_MBE_MAE_MAPE(output_test,EvaSamOut)
[MSE2,RMSE2,MBE2,MAE2,MAPE2] =MSE_RMSE_MBE_MAE_MAPE(output_test,elmanoutput1)
R1 = R_2(output_test,EvaSamOut)
R2 = R_2(output_test,elmanoutput1)
figure(1)
grid
hold on
plot((BestFit),'r');
title(['粒子群算法优化elman ' '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(elmanoutput1,'-b')
hold on
plot(output_test,'-r');
legend('粒子群优化elman预测输出','elman预测输出','期望输出')
title('粒子群优化elman网络预测输出','fontsize',12)
ylabel('函数输出','fontsize',12)
xlabel('样本','fontsize',12)
figure(10)
grid
plot(EvaSamOut,'-g')
hold on
hold on
plot(output_test,'-r');
legend('粒子群优化elman预测输出','期望输出')
title('粒子群优化elman网络预测输出','fontsize',12)
ylabel('负荷','fontsize',12)
xlabel('样本','fontsize',12)
figure(5)
plot(elmanoutput1,'-g')
hold on
plot(output_test,'-k');
legend('预测输出','期望输出')
title('elman神经网络','fontsize',12)
ylabel('函数输出','fontsize',12)
xlabel('样本','fontsize',12)
%预测误差
error1=elmanoutput1-output_test;
junfanggen = mse(elmanoutput1-output_test);
figure(6)
plot(error1,'-k')
title('elman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,'k-');
title('粒子群优化elman神经网络预测误差')
% axis([1 2500 -0.2 0.2])