layrecnet

Layer recurrent neural network

Syntax

layrecnet(layerDelays,hiddenSizes,trainFcn)

Description

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.

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

layerDelays

Row vector of increasing 0 or positive delays (default = 1:2)

hiddenSizes

Row vector of one or more hidden layer sizes (default = 10)

trainFcn

Training function (default = 'trainlm')

and returns a layer recurrent neural network.

Examples

Recurrent Neural Network

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

See Also

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Introduced in R2010b

Deep Learning Toolbox Documentation

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