newc (Neural Network Toolbox)

newc	Examples See Also

Create a competitive layer

Syntax

net = newc(PR,S,KLR,CLR)

Description

Competitive layers are used to solve classification problems.

net = newc(PR,S,KLR,CLR) takes these inputs,

PR - R x 2 matrix of min and max values for R input elements.

S - Number of neurons.

KLR - Kohonen learning rate, default = 0.01.

CLR - Conscience learning rate, default = 0.001.

and returns a new competitive layer.

Properties

Competitive layers consist of a single layer with the negdist weight function, netsum net input function, and the compet transfer function.

The layer has a weight from the input, and a bias.

Weights and biases are initialized with midpoint and initcon.

Adaption and training are done with adaptwb and trainwb1, which both update weight and bias values with the learnk and learncon learning functions.

Examples

Here is a set of four two-element vectors P.

P = [.1 .8  .1 .9; .2 .9 .1 .8];

To competitive layer can be used to divide these inputs into two classes. First a two neuron layer is created with two input elements ranging from 0 to 1, then it is trained.

net = newc([0 1; 0 1],2);
net = train(net,P);

The resulting network can then be simulated and its output vectors converted to class indices.

Y = sim(net,P)
Yc = vec2ind(Y)