nnt2c (Neural Network Toolbox™)

Neural Network Toolbox™

nnt2c

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Update NNT 2.0 competitive layer

Syntax

```
net = nnt2c(PR,W,KLR,CLR)
```

Description

nnt2c(PR,W,KLR,CLR) takes these arguments,

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

W
S x R weight matrix

KLR
Kohonen learning rate (default = 0.01)

CLR
Conscience learning rate (default = 0.001)

`PR`	`R` x `2` matrix of min and max values for `R` input elements
`W`	`S` x `R` weight matrix
`KLR`	Kohonen learning rate (default = 0.01)
`CLR`	Conscience learning rate (default = 0.001)

and returns a competitive layer.

Once a network has been updated, it can be simulated, initialized, adapted, or trained with sim, init, adapt, or train.

See Also

newc

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nftool nnt2elm

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