NARX model training in the Neural Network Tool Box

2013 10 3
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更新时间：2013 10 13
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I have Two questions concerning NARX neural network model when training is done using the early stopping technique:
  1. It is true that NARX model is trained with a static configuration?
  2. If yes, which one of the errors is used to stop training : error (between actual data/predicted data) calculated using a feed back or without feed back like the classic MLP static network? (please see figure below)
Thank your in advance for your response.

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Greg Heath
Greg Heath 2013-10-7
编辑：Greg Heath 2013-10-7

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Narnet and narxnet have two modes: OPENLOOP and CLOSELOOP.
The training of these and most of the MATLAB NNs is stopped via te FIRST of 6 conditions. For example,
net = narxnet % NO SEMICOLON
TrainParams = net.trainParam % NO SEMICOLON
% Partial results
trainParam: .showWindow, .showCommandLine, .show, .epochs,
.time, .goal, .min_grad, .max_fail, .mu, .mu_dec,
.mu_inc, .mu_max
TrainParams =
Function Parameters for 'trainlm'
Maximum Epochs epochs: 1000
Maximum Training Time time: Inf
Performance Goal goal: 0
Minimum Gradient min_grad: 1e-005
Maximum Validation Checks max_fail: 6
Maximum mu mu_max: 10000000000
The OPENLOOP mode is static and only used for training with target data fed into the feedback delay nodes. However, when it is converted to CLOSELOOP for operational use, the accuracy can be worse than if it had been directly trained in the CLOSELOOP mode.
The CLOSELOOP configuration is dynamic and is used for both training and use in the operational mode. However, CLOSELOOP training is very slow compared to OPENLOOP training and the final results may not match well with the target data.
Therefore, there are three approaches to training an operational CLOSELOOP net.
1. Train and operate CLOSELOOP
2. Train OPENLOOP and convert to CLOSELOOP for operational use
3. Train OPENLOOP, convert to CLOSELOOP to train further and use for operation.
The third method tends to yield the best results.
Hope this helps
  • Thank you for formally accepting my answer*
Greg

更多回答（2 个）

Platon
Platon 2013-10-7

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Thank you Greg.
So, when I use this code :
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
Training is done following which approache? it seems to me that is the second one (Train OPENLOOP and convert to CLOSELOOP for operational use). How could I train the net in OPENLOOP, convert to CLOSELOOP to train further and use for operation? is there a specific functions in Mathlab to do that or I have to code it?
Greg Heath
Greg Heath 2013-10-8

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See the examples in
help preparets
help closeloop
Also see if the doc explanations and examples are different.
Hope this helps.
Thank you for formally accepting my answer
Greg
P.S. Change names when converting to the closeloop configuration
netc = closeloop(net);

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Platon
Platon 2013-10-9
I get it, to train in close loop I have to put the closed NN in the train function :
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
netc = closeloop(net);
[netc,tr] = train(netc,inputs,targets,inputStates,layerStates);
But how can I do to train it successively in open loop and then in the closed loop ?
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
netc = closeloop(net);
[netc,tr] = train(netc,inputs,targets,inputStates,layerStates);
??
Greg Heath
Greg Heath 2013-10-9
Almost!
You forgot to use preparets before training and to use the outputs of preparets in train.
Go back and see
help preparets
help closeloop
Platon
Platon 2013-10-9
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
[inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
netc = closeloop(net);
[inputs,inputStates,layerStates,targets] = preparets(netc,inputSeries,{},targetSeries);
[netc,tr] = train(netc,inputs,targets,inputStates,layerStates);
It is correct now?
Greg Heath
Greg Heath 2013-10-13
OK. Now try it.
I always compare the close-loop and open-loop responses before close-loop training to get an appreciation for what the additional training really did.
And sometimes I train close-loop from the beginning to see how much pre-training with open-loop really helps w.r.t. training time.

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