Matlab -Neural Network Simulation (for Loop)

Question

I am quiet new to matlab NN toolbox and have created the following NN network:

val.P=Exp;

net =newff(minmax(p),[20,3],{'tansig','purelin'},'trainlm');

net.trainParam.epochs = 5000;    %Max Ephocs
net.trainParam.goal = 1e-5;     %Training Goal in Mean Sqared Error
net.trainParam.min_grad = 0.05e-3;
net.trainParam.show = 50;       %# of ephocs in display
net.trainParam.max_fail =20;
net = init(net);

[net,tr]=train(net,p,t,[],[],val);
o1 = sim(net,Exp)

How can I run the above for say 20 times and store the data in one variable (o1)? Any help is very much appreciated !

Hi, Please see the revised question.

Esan
– Esan

2012-05-10 13:38:46 +00:00
Commented May 10, 2012 at 13:38 — Esan
– Esan, Commented May 10, 2012 at 13:38

JiaYow · Accepted Answer · 2012-05-10 14:17:07Z

2

for iteration=1:20
  % Your NN code
  [net, tr]=train(net,p,t,[],[],val);
  o1(:,iteration) = sim(net,Exp);
end

After that, o1 will be an array with all the results in it.

Note: Since I don't know the dimensions of your data, you might need to modify o1(iteration) to o1(:,iteration) or o1(:,:,iteration) etc. Whatever you need.

edited May 10, 2012 at 14:17

answered May 10, 2012 at 13:47

JiaYow

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7 Comments

Esan Over a year ago

Thanks alot for the reply, but matlab gives the following error:??? o1[iteration] = sim(net,Exp); | Error: Unbalanced or unexpected parenthesis or bracket.

Esan Over a year ago

o1 is <3x1> after 20 iteration I want matlab to give me a <3x20> o1. I tried o1(iteration,:) = sim(net,Exp); and this is what matlab gives me ??? Subscripted assignment dimension mismatch.

Dan Over a year ago

Exactly what I was going to answer. Personally I would go for o1(:, iteration), that way something like sum(o1 - TestData) will give you a result for each experiment. Also o1(:,:,iteration) etc just makes more sense to me...

Dan Over a year ago

without the loop what does this give you: o1 = sim(net,Exp); size(o1)?

Esan Over a year ago

@Jia: Thank you so much. o1(:, iteration) Solved the problem.

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