newc (Neural Network Toolbox)

Neural Network Toolbox

newc

Create a competitive layer

Syntax

net = newc

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

Description

Competitive layers are used to solve classification problems.

net = newc creates a new network with a dialog box.

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 trains and trainr, 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];
```

A 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)
```

See Also

sim, init, adapt, train, trains, trainr, newcf

network newcf