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
timedelaynet) and distributed delay (
distdelaynet) neural networks, which have finite input responses.
layrecnet(layerDelays,hiddenSizes,trainFcn) takes these arguments,
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