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 (
which has delays on the layer weights in addition to the input weight.
Row vector of increasing 0 or positive delays (default = 1:2)
Row vector of one or more hidden layer sizes (default = 10)
Training function (default =
and returns a time delay neural network.
Here a time delay neural network is used to solve a simple time series problem.
[X,T] = simpleseries_dataset; net = timedelaynet(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 = 0.0225