Elman neural network
Elman networks are feedforward networks (
with the addition of layer recurrent connections with tap delays.
With the availability of full dynamic derivative calculations
bttderiv), the Elman network is no longer
recommended except for historical and research purposes. For more
accurate learning try time delay (
layer recurrent (
narxnet), and NAR (
narnet) neural networks.
Elman networks with one or more hidden layers can learn any
dynamic input-output relationship arbitrarily well, given enough neurons
in the hidden layers. However, Elman networks use simplified derivative
which ignores delayed connections) at the expense of less reliable
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 an Elman neural network.
Here an Elman neural network is used to solve a simple time series problem.
[X,T] = simpleseries_dataset; net = elmannet(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)