nnt2c (Neural Network Toolbox)

Update NNT 2.0 competitive layer to NNT 3.0

net = nnt2c(pr,w,klr,clr)

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.

and returns a competitive layer.

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