elmannet

(To be removed) Elman neural network

elmannet will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Syntax

elmannet(layerdelays,hiddenSizes,trainFcn)


Description

Elman networks are feedforward networks (feedforwardnet) with the addition of layer recurrent connections with tap delays.

With the availability of full dynamic derivative calculations (fpderiv and bttderiv), the Elman network is no longer recommended except for historical and research purposes. For more accurate learning try time delay (timedelaynet), layer recurrent (layrecnet), NARX (narxnet), and NAR (narnet) neural networks.

Elman networks with one or more hidden layers can learn any dynamic input-output relationship arbitrarily well, given enough neurons in the hidden layers. However, Elman networks use simplified derivative calculations (using staticderiv, which ignores delayed connections) at the expense of less reliable learning.

elmannet(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 an Elman neural network.

Examples

Train Elman Neural Network

This example shows how to train an Elman neural network to solve a simple time series problem.

[X,T] = simpleseries_dataset;
net = elmannet(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,Ts,Y)

perf = 
9.0122e-04

Version History

Introduced in R2010b

R2026a: elmannet will be removed

elmannet will be removed in a future release.

To train a neural network with time series data, use one of these options:

• Time Series Modeler app — Interactively design and train neural networks for time series data.

• trainnet — Train neural network. Use this function when you need more flexibility in the network architecture or training algorithm.

To learn more about time series data workflows, see these examples and topics:

• Training Neural Networks with Time Series Data

• Time Series Forecasting Using Deep Learning

• Create and Train NARX Network for Time Series Forecasting

• Long Short-Term Memory Neural Networks

• Example Deep Learning Network Architectures

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.