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Create feed-forward backpropagation network with feedback from output to input
newnarx(P,T,ID,OD,[S1 S2...S(N-l)],{TF1 TF2...TFNl}, BTF,BLF,PF,IPF,OPF,DDF) takes
and returns an N-layer feed-forward backpropagation network with external feedback.
The transfer function TFi can be any differentiable transfer function such as tansig, logsig, or purelin.
The d delays from output to input FBD must be integer values greater than zero placed in a row vector.
The training function BTF can be any of the backpropagation training functions such as trainlm, trainbfg, trainrp, traingd, etc.
| Caution trainlm is the default training function because it is very fast, but it requires a lot of memory to run. If you get an out-of-memory error when training, try one of these: |
The learning function BLF can be either of the backpropagation learning functions learngd or learngdm.
The performance function can be any of the differentiable performance functions such as mse or msereg.
Here is a problem consisting of sequences of inputs P and targets T to be solved with a network.
P = {[0] [1] [1] [0] [-1] [-1] [0] [1] [1] [0] [-1]}; T = {[0] [1] [2] [2] [1] [0] [1] [2] [1] [0] [1]};
A two-layer feed-forward network with a two-delay input and two-delay feedback is created. The network has one hidden layer of five tansig neurons.
The network is simulated and its output plotted against the targets.
The network is trained for 50 epochs. Again the network's output is plotted.
Feed-forward networks consist of Nl layers using the dotprod weight function, netsum net input function, and the specified transfer functions.
The first layer has weights coming from the input. Each subsequent layer has a weight coming from the previous layer. All layers have biases. The last layer is the network output.
Each layer's weights and biases are initialized with initnw.
Adaption is done with trains, which updates weights with the specified learning function. Training is done with the specified training function. Performance is measured according to the specified performance function.
newcf, newelm, sim, init, adapt, train, trains
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