MATLAB: When training a NARX network during the validation step, does the network use the outputs it calculates to feedback ? Or does it use the outputs you provide

neural networks

Hi,

When training a NARX network during the validation phase, does the network use the output it calculates to feedback? Or does it use the value from the target data set ?

Thanks

Best Answer

It uses both:

 MODE 
 Open-Loop (OL)  Starts with TARGETS in the feedback connections
 Close-Loop (CL) Finishes with OUTPUTS in the feedback connections

Thank you for formally accepting my answer

Greg

Related Solutions

MATLAB: How to test and improve multi layer perceptron

NEWPR is the version of NEWFF that should be used for classification/pattern-recognition

Remove ending semicolons to see command line printout:

 target           = ind2vec(trueclassindices);% Use for training
trueclassindices = vec2ind(target);
...
output = net(input);
predictedclassindices = vec2ind(output);
err = predictedclassindices ~= trueclassindices; % ones and zeros
Nerr = sum(err);
PctErr = 100*Nerr/N

classification breakdowns of the trn/val/tst subsets can be obtained using the training record tr obtained from

 [ net tr output error  ] = train(net,input,target);
 % output = net(input);
 % error = target-output;
 tr = tr    % No semicolon to see the goodies

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: NARX test set closed loop

You seem confused:

 1. OL trn, val and tst subsets are automatically 
    tested by TRAIN.
 2. The OL configuration is then explicitly 
    converted to CL by CLOSELOOP.
 3. CL performance of the trn, val and tst subsets 
    is then obtained after the conversion.

You can see some recent examples by searching in BOTH NEWSREADER and ANSWERS using

 greg narxnet closeloop

Hope this helps.

Thank you for formally accepting my answer

Greg

Best Answer

Related Solutions

MATLAB: How to test and improve multi layer perceptron

MATLAB: NARX test set closed loop

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