timedelaynet

(To be removed) Time delay neural network

timedelaynet 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

timedelaynet(inputDelays,hiddenSizes,trainFcn)

Description

timedelaynet(inputDelays,hiddenSizes,trainFcn) takes these arguments:

• Row vector of increasing 0 or positive input delays, inputDelays

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

• Training function, trainFcn

and returns a time delay neural network.

Time delay networks are similar to feedforward networks, except that the input weight has a tap delay line associated with it. This allows the network to have a finite dynamic response to time series input data. This network is also similar to the distributed delay neural network (distdelaynet), which has delays on the layer weights in addition to the input weight.

Examples

Train Time Delay Network and Predict on New Data

This example shows how to train a time delay network.

Partition the training set. Use Xnew to do prediction in closed loop mode later.

[X,T] = simpleseries_dataset;
Xnew = X(81:100);
X = X(1:80);
T = T(1:80);

Train a time delay network, and simulate it on the first 80 observations.

net = timedelaynet(1:2,10);
[Xs,Xi,Ai,Ts] = preparets(net,X,T);
net = train(net,Xs,Ts,Xi,Ai);

view(net)

Calculate the network performance.

[Y,Xf,Af] = net(Xs,Xi,Ai);
perf = perform(net,Ts,Y);

Run the prediction for 20 timesteps ahead in closed loop mode.

[netc,Xic,Aic] = closeloop(net,Xf,Af);
view(netc)

y2 = netc(Xnew,Xic,Aic);

Input Arguments

inputDelays — 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'

Data Types: char

Version History

Introduced in R2010b

R2026a: timedelaynet will be removed

timedelaynet 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.