PSO.rar_pso 神经网络_soil_土壤水分_水分matlab_神经网络水分

function [NewW1,NewB1,NewW2,NewB2]=PSOTrain(TrainSamIn,TrainSamOut,HiddenUnitNum) MG=700;%最大迭代次数 E0=0.0001;%允许的误差 Xmin=-10; Xmax=10; Vmin=-5; Vmax=5; M=100;%种群个数 c1=2.7; c2=2.5; w=0.9; [R,SamNum]=size(TrainSamIn); [S,SamNum]=size(TrainSamOut); g=1; d=0; b1=zeros(HiddenUnitNum,R+S+1,M); b2=zeros(S,M); g1=zeros(HiddenUnitNum,R+S+1,M); g2=zeros(S,M); E=zeros(size(TrainSamOut));%保存误差 startp1=rand(HiddenUnitNum,R+S+1,M)-0.5;%产生初始的权值 startp2=rand(S,M)-0.5; startv1=rand(HiddenUnitNum,R+S+1,M)-0.5;%产生初始的权值 startv2=rand(S,M)-0.5; endp1=zeros(HiddenUnitNum,R+S+1,M);%产生初始的权值 endp2=zeros(S,M); endv1=zeros(HiddenUnitNum,R+S+1,M);%产生初始的权值 endv2=zeros(S,M); startE=zeros(1,M); endE=zeros(1,M); for i=1:M W1=startp1(1:HiddenUnitNum,1:R,i); W2=startp1(1:HiddenUnitNum,R+1:R+S,i); B1=startp1(1:HiddenUnitNum,R+S+1,i); B2=startp2(1:S,i); for j=1:SamNum TempOut=tansig(W1*TrainSamIn(:,j)+B1); NetWorkOut(1,j)=W2'*TempOut+B2; end E=NetWorkOut-TrainSamOut; startE(1,i)=sum(E)/(SamNum*S); b1(:,:,i)=startp1(:,:,i); b2(:,i)=startp2(:,i); end [minE,index]=min(startE(1,:));%找出误差的最小值，并找到相应的指标 g1=startp1(:,:,index);%自身的最优位置 g2=startp2(:,index);%群体的最优位置 pgvalue=minE; pgvalue_last=pgvalue; while (~d) for k=1:M endv1(:,:,k)=w*startv1(:,:,k)+c1*rand*(b1(:,:,k)-startp1(:,:,k))+c2*rand*(g1-startp1(:,:,k)); endv2(:,k)=w*startv2(:,k)+c1*rand*(b2(:,k)-startp2(:,k))+c2*rand*(g2-startp2(:,k)); for i=1:HiddenUnitNum%将速度保持在参数的范围内 for j=1:(R+S+1) endv1(i,j,k)=endv1(i,j,k); if endv1(i,j,k)>Vmax endv1(i,j,k)=Vmax; elseif endv1(i,j,k)<Vmin endv1(i,j,k)=Vmin; end end end for s=1:S endv2(s,k)=endv2(s,k); if endv2(s,k)>Vmax endv2(s,k)=Vmax; elseif endv2(s,k)<Vmin endv2(s,k)=Vmin; end end endp1(:,:,k)=startp1(:,:,k)+endv1(:,:,k);%位置更新 endp2(:,k)=startp2(:,k)+endv2(:,k);%位置更新 for i=1:HiddenUnitNum%将位置保持在参数的范围内 for j=1:(R+S+1) if endp1(i,j,k)>Xmax endp1(i,j,k)=Xmax; elseif endp1(i,j,k)<Xmin endp1(i,j,k)=Xmin; end end end for s=1:S if endp2(s,k)>Xmax endp2(s,k)=Xmax; elseif endp2(s,k)<Xmin endp2(s,k)=Xmin; end end W1=endp1(1:HiddenUnitNum,1:R,k); W2=endp1(1:HiddenUnitNum,R+1:R+S,k); B1=endp1(1:HiddenUnitNum,R+S+1,k); B2=endp2(1:S,k); for p=1:SamNum TempOut=tansig(W1*TrainSamIn(:,p)+B1); NetWorkOut(1,p)=W2'*TempOut+B2; end E=NetWorkOut-TrainSamOut; endE(1:k)=sumsqr(E)/(SamNum*S); if endE(1,k)<startE(1,k) b1(:,:,k)=endp1(:,:,k); b2(:,k)=endp2(:,k); startE(1,k)=endE(1,k); end end w=0.9-(0.5/MG)*g; [value,index]=min(startE(1,:)); if value<pgvalue g1=b1(:,:,index); g2=b2(:,index); pgvalue=value; end if g>=MG d=1 end if pgvalue<E0 d=1; end startp1=endp1; startp2=endp2; startv1=endv1; startv2=endv2; startE=endE; g=g+1; end W1=g1(1:HiddenUnitNum,1:R); W2=g1(1:HiddenUnitNum,R+1:R+S); B1=g1(1:HiddenUnitNum,R+S+1); B2=g2(:,1); NewW1=W1; NewW2=W2; NewB1=B1; NewB2=B2;