Layer recurrent neural network
Layer recurrent neural networks are similar to feedforward networks,
except that each layer has a recurrent connection with a tap delay
associated with it. This allows the network to have an infinite dynamic
response to time series input data. This network is similar to the
time delay (
distributed delay (
neural networks, which have finite input responses.
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 layer recurrent neural network.
Use a layer recurrent neural network to solve a simple time series problem:
[X,T] = simpleseries_dataset; net = layrecnet(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,Y,Ts)
perf = 6.1239e-11