英语翻译newelm PurposeCreate Elman backpropagation network Syntaxnet = newelm(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl},BTF,BLF,PF,IPF,OPF,DDF)Descriptionnewelm(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl},BTF,BLF,PF,IPF,OPF,DDF) takes these arguments,PR x

来源：学生作业帮助网编辑：六六作业网时间：2024/12/26 00:47:26

英语翻译newelmPurposeCreateElmanbackpropagationnetworkSyntaxnet=newelm(P,T,[S1S2...S(N-l)],{TF1TF2...TFN

英语翻译newelm PurposeCreate Elman backpropagation network Syntaxnet = newelm(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl},BTF,BLF,PF,IPF,OPF,DDF)Descriptionnewelm(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl},BTF,BLF,PF,IPF,OPF,DDF) takes these arguments,PR x
英语翻译
newelm
Purpose
Create Elman backpropagation network
Syntax
net = newelm(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl},
BTF,BLF,PF,IPF,OPF,DDF)
Description
newelm(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl},BTF,BLF,PF,IPF,OPF,DDF) takes these arguments,
PR x Q1 matrix of Q1 sample R-element input vectorsTSN x Q2 matrix of Q2 sample SN-element input vectorsSiSize of ith layer,for N-1 layers,default = [ ].
(Output layer size SN is determined from T.)TFiTransfer function of ith layer.(Default = 'tansig' for
hidden layers and 'purelin' for output layer.)BTFBackpropagation network training function (default = 'trainlm')BLFBackpropagation weight/bias learning function (default = 'learngdm')PFPerformance function (default = 'mse')IPFRow cell array of input processing functions.(Default = {'fixunknowns','removeconstantrows','mapminmax'})OPFRow cell array of output processing functions.(Default = {'removeconstantrows','mapminmax'})DDFData divison function (default = 'dividerand')
and returns an Elman network.
The training function BTF can be any of the backpropagation training functions such as trainlm,trainbfg,trainrp,traingd,etc.Warning For Elman networks,we do not recommend algorithms that take large step sizes,such as trainlm and trainrp.Because of the delays in Elman networks,such algorithms only approximate the performance gradient.This makes learning difficult for large-step algorithms.
The learning function BLF can be either of the backpropagation learning functions learngd or learngdm.
The performance function can be any of the differentiable performance functions such as mse or msereg.
Examples
Here is a series of Boolean inputs P,and another sequence T,which is 1 wherever P has two 1s in a row.
P = round(rand(1,20));
T = [0 (P(1:end-1)+P(2:end) == 2)];
You want the network to recognize whenever two 1s occur in a row.First,arrange these values as sequences.
Pseq = con2seq(P);
Tseq = con2seq(T);
Next,create an Elman network with one hidden layer of ten neurons.
net = newelm(P,T,10);
Then train the network and simulate it.
net = train(net,Pseq,Tseq);
Y = sim(net,Pseq)
Algorithm
Elman networks consist of Nl layers using the dotprod weight function,netsum net input function,and the specified transfer function.
The first layer has weights coming from the input.Each subsequent layer has a weight coming from the previous layer.All layers except the last have a recurrent weight.All layers have biases.The last layer is the network output.
Each layer's weights and biases are initialized with initnw.
Adaption is done with trains,which updates weights with the specified learning function.Training is done with the specified training function.Performance is measured according to the specified performance function.
不要用在线翻译

这个是newelm函数的使用说明~