bp.m

BP神经网络功能的实现

%function train_net=train_net()
%训练神经网络函数

load target.txt;
t = target';
load input.txt;
p = input';
[pn,minp,maxp,tn,mint,maxt]=premnmx(p,t);

net = newff(minmax(pn),[12,3],{'tansig', 'purelin'},'trainlm','learngd'); 
net.layers{1}.initFcn = 'initwb';
net.inputWeights{1,1}.initFcn = 'rands'; 
net.biases{1,1}.initFcn = 'rands'; 
net.biases{2,1}.initFcn = 'rands'; 
net = init(net); 

%训练参数的设定 
net.trainParam.epochs = 3000 % Maximum number of epochs to train 
net.trainParam.goal = 0.001 % 0 Performance goal 
net.trainParam.max_fail = 5 % Maximum validation failures 
net.trainParam.mem_reduc = 1 % Factor to use for memory/speed trade off. 
net.trainParam.min_grad = 1e-6% Minimum performance gradient 
net.trainParam.show = 5 % 25 Epochs between displays (NaN for no displays) 
net.trainParam.time = inf % Maximum time to train in seconds 
net.trainParam.mu = 0.001              %  Initial Mu
net.trainParam.mu_dec = 0.1            %  Mu decrease factor
net.trainParam.mu_inc = 10             %  Mu increase factor

LP.lr = 0.5 % 0.01 Learning rate 

net = train(net,pn,tn); 
save mynet net
%end