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timedelaynet

Time delay neural network

Description

example

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

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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

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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
'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'

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

Introduced in R2010b