MATLAB: How to conduct a sensitivity analysis in neural network model with Nominal Data

Deep Learning Toolboxneural networksensitivity analysis

Hello world,

I have built a neural network for pattern recognition (patternnet) and i would like to conduct a sensitivity analysis, however, the main problem is that I have only Nominal Data as input and that makes the noise addition procedure kind of mistaken. Any ideas?

Best regards,

Andreas Kampianakis

Best Answer

Your net is probably so dumb that it doesn't know the inputs are supposed to be nominal (I assume you mean 1s and 0s).

So, plotting performance vs magnitude of added Gaussian noise should be useful.

Hope this helps.

Greg

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MATLAB: How to compute sensitivity analysis in neural network model

1. Use MAPSTD or ZSCORE to standardize the data BEFORE training.

2. Design 10 or more successful nets with the smallest number of hidden nodes as possible.

3. For each input: Add Gaussian noise to only that input. Tabulate and plot MSE vs noise standard deviation

There are also many, many, approaches for ranking inputs via backward and forward searches. In backward search, replacing an input with zeros (it's mean value) is equivalent to removing it.

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: How to train a feedfordward neural network with error weights

 Use the command window
 help mse
 doc mse

Hope this helps.

Thank you for formally accepting my answer

Greg

Best Answer

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MATLAB: How to compute sensitivity analysis in neural network model

MATLAB: How to train a feedfordward neural network with error weights

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