newnarx initialization and validation problem

Dear all,

I am working on the problem: y(k)=0.3*y(k-1)+0.6*y(k-2)+u(k)^3+0.3*u(k)^2-0.4*u(k) using narx. the code is listed below.

net = newnarx(u,yn,0,1:2,[15 10],{'tansig','tansig','purelin'},'trainscg');

net.trainParam.lr = 0.05; net.trainParam.lr_inc = 1.05;

net.trainParam.lr_dec = 0.7; net.trainParam.hide = 50;

net.trainParam.mc = 0.9; net.trainParam.epochs = s;

net.trainParam.goal = 1e-8; net.trainParam.time = 5*3600; [trainP,valP,testV,trainInd,valInd,testInd] = divideblock(u,0.6,0.2,0.2);

[trainT,valT,testT] = divideind(yn,trainInd,valInd,testInd);

net.divideFcn='divideblock';

net = init(net);

My training stops after 6 validation checks. Why? The trained net gives different outputs each time. Is there any mistake in the initialization?

Please help.

Answer 1

[Greg Heath]

See my previous post re your last program. Most of the comments are relevant for this post.

In particular, do not overwrite defaults unless you have a darned good reason.

Also, it is very seldom that a 2nd hidden layer is necessary.

Validation stopping was created to make sure the trained net is useful for nontraining data. Stopping after MSEval increases for 6 consecutive epochs is reasonable.

You will get different results every time because the initial weights are random.

For each different setting of delays and hidden nodes I usually loop over Ntrials = 10 designs.

You would be surprised how many times only 7 or 8 out of 10 are acceptable.

However, I do initialize the random number generator before the double loop over hidden nodes and random weight initializations.

Hope this helps.

Thank you for formally accepting my answer.

Greg

[Unnikrishnan PC]

Thank you Greg for the fast reply.