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Time delay neural network



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.


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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);


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);

y2 = netc(Xnew,Xic,Aic);

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