一、 介绍新版newff
Syntax
· net = newff(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl}, BTF,BLF,PF,IPF,OPF,DDF)
Description
newff(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl}, BTF,BLF,PF,IPF,OPF,DDF) takes several arguments
P
R x Q1 matrix of Q1 sample R-element input vectors
T
SN x Q2 matrix of Q2 sample SN-element target vectors
Si
Size of ith layer, for N-1 layers, default = [ ].
(Output layer size SN is determined from T.)
TFi
Transfer function of ith layer. (Default = 'tansig' for
hidden layers and 'purelin' for output layer.)
BTF
Backpropagation network training function (default = 'trainlm')
BLF
Backpropagation weight/bias learning function (default = 'learngdm')
IPF
Row cell array of input processing functions. (Default = {'fixunknowns','removeconstantrows','mapminmax'})
OPF
Row cell array of output processing functions. (Default = {'removeconstantrows','mapminmax'})
DDF
Data divison function (default = 'dividerand')
Examples
Here is a problem consisting of inputs P and targets T to be solved with a network.
· P = [0 1 2 3 4 5 6 7 8 9 10];T = [0 1 2 3 4 3 2 1 2 3 4];
Here a network is created with one hidden layer of five neurons.
· net = newff(P,T,5);
The network is simulated and its output plotted against the targets.
· Y = sim(net,P);plot(P,T,P,Y,'o')
The network is trained for 50 epochs. Again the network's output is plotted.
· net.trainParam.epochs = 50;net = train(net,P,T);Y = sim(net,P);
plot(P,T,P,Y,'o')