可动态调整的神经网络控制器matlab仿真,含仿真操作录像

clc; clear; close all; K=1; T=10; Ts=2; ks=0.707; num=K; den=[T^2,2*ks*T,1]; G=tf(num,den) Gd=c2d(G,Ts) [numd,dend]=c2dm(num,den,Ts) alldatanum=1000; traindatanum=500; testdatanum=500; u=rands(1,alldatanum+10); y=zeros(1,alldatanum+10); for i=3:alldatanum+2 y=dlsim(numd,dend,u); end alldatain=[]; alldataout=[]; for i=3:alldatanum+2 newin=[u(i-1);y(i-1);u(i-2);y(i-2)]; alldatain=[alldatain newin]; alldataout=[alldataout y(i)]; end traindatain=alldatain(:,1:traindatanum); traindataout=alldataout(:,1:traindatanum); testdatain=alldatain(:,traindatanum+1:alldatanum); testdataout=alldataout(:,traindatanum+1:alldatanum); figure hold on grid j=traindatanum+1:alldatanum plot(j,testdatain,'k-') xlabel('input t'); ylabel('output u'); figure hold on grid j=traindatanum+1:alldatanum plot(j,testdataout,'b-') xlabel('input t'); ylabel('output y'); indim=4; outdim=1; hiddenunitnum=10; maxepochs=10000; E0=0.000001; center=1*rand(indim,hiddenunitnum)-0.5; sp=0.2*rand(1,hiddenunitnum)+0.1; w=0.2*rand(1,hiddenunitnum)-0.1; lrcent=0.001; lrsp=0.001; lrw=0.001; errorhistory=[]; for epoch=1:maxepochs alldist=dist(center',traindatain); spmat=repmat(sp',1,traindatanum); unitout=radbas(alldist./spmat); netout=w*unitout; error=traindataout-netout; SSE=sumsqr(error) errorhistory=[errorhistory SSE]; if SSE<E0,break,end for k=1:hiddenunitnum centgrad=(traindatain-repmat(center(:,k),1,traindatanum))... *(error.*unitout(k,:)*w(k)/(sp(k)^2))'; spgrad=alldist(k,:).^2*(error.*unitout(k,:)*w(k)/(sp(k)^3))'; wgrad=error*unitout(k,:)'; center(:,k)=center(:,k)+lrcent*centgrad; sp(k)=sp(k)+lrsp*spgrad; w(k)=w(k)+lrw*wgrad; end end epoch testdistance=dist(center',testdatain); testspreadsmat=repmat(sp',1,testdatanum); testhiddenunitout=radbas(testdistance./testspreadsmat); testnnout=w*testhiddenunitout; plot(j,testnnout,'r-') figure hold on grid [xx,num]=size(errorhistory); plot(1:num,errorhistory,'r-');