Using different learning algorithms for the neural net toolkit
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How do I go about implementing a genetic algorithm (for example) tool to minimise the weights for a neural network to find the global maxima? I'm worried that the built in trainers are not adequate to find the global minima.
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Greg Heath
2016-7-28
编辑：Greg Heath
2016-8-5
You may be worrying about the wrong thing. With a typical I-H-O FFnet the number of equivalent nets obtained by just changing weight signs and index order is ~
2^H * factorial(H) (= 3.7159e+09 for the
default H=10)
I find the best bet is to just find one of the many nets that MINIMIZE THE NUMBER OF HIDDEN NODES subject to the following maximum bound on mean-square-error.
MSE = mse(error) <= 0.001*MSE00
where
error = target - output;
MSE00 = mean(var(target',1)) % Average target variance
The resulting bounds on normalized MSE and Rsquare (Google R squared) are
NMSE = MSE/MSE00 <= 0.001
Rsq = 1 - NMSE >= 0.999
which is interpreted as successfully modelling more than 99.9% of the target variance.
Initial weights are random. Therefore it is wise to make a double loop search over number of hidden nodes and initial random number states. I have posted zillions of examples in both the NEWSGROUP and ANSWERS. Good search words are
greg Hmin Hmax Ntrials
If you insist on using a genetic algorithm see my post
Hope this helps.
Thank you for formally accepting my answer
Greg
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