How to specify the transfer function for the hidden and output layer?

I am using feedforwardnet to create a neural network. But this function takes only two arguments: the hidden layer sizes and the training function. I want to specify the transfer functions for each layer.

Answer 1

[Greg Heath]

Use fitnet for regression/curve-fitting or patternnet for classification/pattern-recognition. Both call feedforwardnet.

2 layers are sufficient: 1 hidden + 1 output

Standardize(0-mean/unit-variance via zscore or mapstd) inputs and regression outputs.

Remove or modify outliers

Convert classification targets to 0-1 unit vectors via ind2vec

Accept the fitnet default transfer functions tansig/purelin

For classification the default tansig/softmax transfers are the better choice

The best way to choose initial weights and number of hidden nodes is via trial and error.

I have posted many double for loop examples. Search the NEWSGROUP and ANSWERS

 greg fitnet  Ntrials    % for regression

 greg patternnet Ntrials % for classification

Hope this helps.

Thank you for formally accepting my answer

Greg