elmannet
Elman neural network
Syntax
elmannet(layerdelays,hiddenSizes,trainFcn)
Description
Elman networks are feedforward networks (feedforwardnet
) with the addition of layer recurrent connections with tap
delays.
With the availability of full dynamic derivative calculations (fpderiv
and bttderiv
), the Elman network is no longer
recommended except for historical and research purposes. For more accurate learning try time
delay (timedelaynet
), layer recurrent (layrecnet
), NARX (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 calculations (using staticderiv
, which ignores delayed connections) at the expense of less reliable
learning.
elmannet(layerdelays,hiddenSizes,trainFcn)
takes these arguments,
layerdelays | 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 an Elman neural network.
Examples
Version History
Introduced in R2010b
See Also
preparets
| removedelay
| timedelaynet
| layrecnet
| narnet
| narxnet