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Layer recurrent neural network



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.


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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);


Y = net(Xs,Xi,Ai);
perf = perform(net,Y,Ts)
perf = 6.1239e-11

Input Arguments

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

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

Training function name, specified as one of the following.

Training FunctionAlgorithm



Bayesian Regularization


BFGS Quasi-Newton


Resilient Backpropagation


Scaled Conjugate Gradient


Conjugate Gradient with Powell/Beale Restarts


Fletcher-Powell Conjugate Gradient


Polak-Ribiére Conjugate Gradient


One Step Secant


Variable Learning Rate Gradient Descent


Gradient Descent with Momentum


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

Version History

Introduced in R2010b