| Neural Network Toolbox | |
Update NNT 2.0 competitive layer
Syntax
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.
and returns a competitive layer.
Once a network has been updated, it can be simulated, initialized, or trained with sim, init, adapt, and train.
See Also
| | nncopy | nnt2elm | |