layrecnet

Layer recurrent neural network

Syntax

layrecnet(layerDelays,hiddenSizes,trainFcn)

Description

layrecnet(layerDelays,hiddenSizes,trainFcn) takes these arguments:

• Row vector of increasing 0 or positive delays, layerDelays

• Row vector of one or more hidden layer sizes, hiddenSizes

• Backpropagation training function, trainFcn

and returns a layer recurrent neural network.

Layer recurrent neural networks are similar to feedforward networks, except that each layer has a recurrent connection with a tap delay associated with it. This allows the network to have an infinite dynamic response to time series input data. This network is similar to the time delay (timedelaynet) and distributed delay (distdelaynet) neural networks, which have finite input responses.

Examples

Recurrent Neural Network

This example shows how to use a layer recurrent neural network to solve a simple time series problem.

[X,T] = simpleseries_dataset;
net = layrecnet(1:2,10);
[Xs,Xi,Ai,Ts] = preparets(net,X,T);
net = train(net,Xs,Ts,Xi,Ai);
view(net)
Y = net(Xs,Xi,Ai);
perf = perform(net,Y,Ts)

perf =

   6.1239e-11

Input Arguments

layerDelays — Input delays

[1:2] (default) | row vector

Zero or positive input delays, specified as an increasing row vector.

hiddenSizes — Hidden sizes

10 (default) | row vector

Sizes of the hidden layers, specified as a row vector of one or more elements.

trainFcn — Training function name

'trainlm' (default) | 'trainbr' | 'trainbfg' | 'trainrp' | 'trainscg' | ...

Training function name, specified as one of the following.

Training Function	Algorithm
'trainlm'	Levenberg-Marquardt
'trainbr'	Bayesian Regularization
'trainbfg'	BFGS Quasi-Newton
'trainrp'	Resilient Backpropagation
'trainscg'	Scaled Conjugate Gradient
'traincgb'	Conjugate Gradient with Powell/Beale Restarts
'traincgf'	Fletcher-Powell Conjugate Gradient
'traincgp'	Polak-Ribiére Conjugate Gradient
'trainoss'	One Step Secant
'traingdx'	Variable Learning Rate Gradient Descent
'traingdm'	Gradient Descent with Momentum
'traingd'	Gradient Descent

Example: For example, you can specify the variable learning rate gradient descent algorithm as the training algorithm as follows: 'traingdx'

For more information on the training functions, see Train and Apply Multilayer Shallow Neural Networks and Choose a Multilayer Neural Network Training Function.

Data Types: char