MATLAB: Is narnet <=> to feedforwardnet where the input(s) is(are) previous value(s) of the ouput

delaysnarneural networkspreparets

Hi everyone.

I recently started my PhD and therefore working with artificial neural networks (ANN's).

I'd like to try an architecture where each parameter (input/output) can have its own delay but first I decided to explore the NAR concept.

From what I understood, for NAR the function "narnet" allows the definition of the output delays and the function "preparets" applies the delays and structures accordingly the parameter vector that will be used for training the ANN.

Is this equivalent to using a feedforwardnet function where I prepare input vector(s) as shifted version(s) of the output and I remove the output initial values (for all to have the same number of elements)?

Thanks in advance, Rodrigo

Best Answer

For deployment, NAR and NARNET must be in the CLOSELOOP configuration which, typically, takes very long to train because an error-free feedback signal is not available.

The use of the OPENLOOP configuration witn known target feedback can allow for much quicker training.

The performance of the openloop configuration can be simulated by a feedforward net using the target matrix as a delayed input. However, it woulds absolutely make no sense to do so because it is not deployable and cannot be converted to the closeloop deployable configuration.

Hope this helps.

Thank you for formally accepting my answer

Greg

Related Solutions

MATLAB: How to use neural network for multi step ahead prediction

Increase the number of feedback delays.

Hope this helps

Thank you for formally accepting my answer

Greg

MATLAB: NARX model training in the Neural Network Tool Box

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

Best Answer

Related Solutions

MATLAB: How to use neural network for multi step ahead prediction

MATLAB: NARX model training in the Neural Network Tool Box

Related Question