timedelaynet
Time delay neural network
Syntax
timedelaynet(inputDelays,hiddenSizes,trainFcn)
Description
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.
timedelaynet(inputDelays,hiddenSizes,trainFcn) takes these arguments,
inputDelays | 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 = |
and returns a time delay neural network.
Examples
Train Time Delay Network and Predict on New Data
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);
See Also
distdelaynet | narnet | narxnet | preparets | removedelay