📄 神经网络.m
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net=newff(PR,[S1,s2,...sn],{tf1 tf2...tfn},btf,blf,pf)
p=[-1 -1 2 2;0 5 0 5];
t=[-1 -1 1 1];
net=newff(minmax(p),[3,1],{'tansig','purelin'},'traingdm');
net.trainparam.show=50;
net.trainparam.lr=0.05;
net.trainparam.mc=0.9;
net.trainparam.epochs=300;
net.trainparam.goal=1e-5;
[net,tr]=train(net,p,t);
a=sim(net,p)
%traingda,trainrp,traincgf,traincgp,traincgb,trainscg,trainbgf,trainoss,\
%trainlm
%[pn,minp,maxp,tn,mint,maxt]=premnmx(p,t)
%量化到[-1,1]之间,利用postmnmx可以还原
[pn,meanp,stdp,tn,meant,stdt]=prestd(p,t)
[pn,meanp,stdp]=prestd(p)
net=newff(minmax(p),[5,1],{'tansig','purelin'},'trainlm')
net=train(net,pn,tn)
an=sim(net,pn);
a=poststd(an,meant,stdt);
pnewn=trastd(pn,meanp,stdp);
anewn=sim(net,pnewn);
anew=poststd(anewn,meant,stdt);
[m,b,r]=postreg(a,t)
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