The neural network stops before it starts? (minimum gradient reached ) error

Hello every body. I am using Neural network in my research graduation project " Pattern Identification ". I have built a custom neural network, and I used the function 'trainlm' .

The Problem is that, once I start the training process, the training window appears and tells me that the minimum gradient is reached and the neural network stops even before it starts !

( The architecture of my NN is complicated, and I'm using three inputs with 13 layers each of them carries different neurons ! )

I wish I can find someone helping me

Thanks in advance !

[EanX]

I have a similar problem in designing a NARNET.

Currently best performance was obtained with 15 hidden nodes (1 hidden layer), mapminmax for processing function for inputs (ymin=0, ymax=10), tansig as transfer function for layer 1 and poslin for output layer.

I noticed that if I change output transfer function to the more commonly employed purelin I can avoid "minimum gradient reached" issue, but I used poslin to obtain forecasts always positive.

Can anyone enlighten me on this topic?

Thanks in advance !

[Greg Heath]

Do not change defaults until you have obtained the best default result.

Are you looping over 10 or more weight initializations and getting premature MinGrad reached on all of them?

Answer 1

[Greg Heath]

The best way to design a neural net is to first find the simplest architecture that yields a good solution.

My advice is to start with a single hidden layer and find the smallest number of hidden neurons that will yield a solution that cannot be significantly improved by using more neurons.

The best targets for patternnet are unit column vectors with a single unity component. The best training algorithm for that type of target is trainscg.

Hope this helps.

Greg

[Abdullah]

Thanks so much , I will try to apply what you have mentioned and then tell you the results .

Thanks so much :)