RNNC

Random Neural Net classifier

    W = RNNC(A,N,S)

Input
A	Input dataset
N	Number of neurons in the hidden layer
S	Standard deviation of weights in an input layer (default: 1)

Output
W	Trained Random Neural Net classifier

Description

W is a feed-forward neural net with one hidden layer of N sigmoid neurons.  The input layer rescales the input features to unit variance; the hidden  layer has normally distributed weights and biases with zero mean and  standard deviation S. The output layer is trained by the dataset A.  Default N is number of objects * 0.2, but not more than 100.

If N and/or S is NaN they are optimised by REGOPTC.

Uses the Mathworks' Neural Network toolbox.

See also

mappings, datasets, lmnc, bpxnc, neurc, rbnc,