| Library: | nn |
| See also: | rbftest rbfpredict rbftrain rbfload rbfinfo rbftrain2 |
| Quantlet: | rbfsave | |
| Description: | saves given radial basis function network into the given file |
| Usage: | rbfsave(rbfnet,name) | |
| Input: | ||
| rbfnet | composed object (list), RBF network as computed by rbftrain or rbftrain2 | |
| name | string, name of the file which will hold the network | |
All components of the RBF network are composed into a matrix, which is explained in the reference book.
library("nn") ; load the library
; training set
randomize(2020)
n = 10
xt = normal(n,2)+#(-1,-1)' | normal(n,2)+#(+1,+1)'
yt =(matrix(n)-1)|matrix(n)
; build the RBF network
clusters = 2
learn = 0.1|0.2|0.1
epochs = 5|5
mMSE = 0.05
activ = 0
rbfnet = rbftrain(xt,yt,clusters,learn,epochs,mMSE,activ)
rbfsave(rbfnet,"rbf_net")
Saves the RBF network into two files "rbf_net.rbf" and "rbf_net.err" Contents of t [1,] " An 2 - 2 - 1 RBF-network" [2,] " training epochs: 5 - 5" [3,] " cluster's learning rates: 0.2000 - 0.1000" [4,] " output's learning rate: 0.1000" [5,] " minimum mean squared error: 0.050" [6,] " BINARY sigmoid activation function" [7,] " minimum MSE reached: 0.190974 "