MATLAB: Applications where Radial Basis and Probabilistic Neural Networks are successful respectively

Deep Learning Toolboxrbf pnn

Can someone please explain the application areas of Radial Basis and Probabilistic Neural Networks? I mean How to identify where a particular network is successful? I am calculating feature vectors through different techniques. Some are giving results with RBFs while others with PNNs. I am not able to identify reasons for the same.

Thank you

Best Answer

Use RBFs. Like MLPs, under some conditions, they are universal approximators.

I consider PNNs to be a special case of an RBF.

MATLAB's version of an RBF has two nagging defaults.

 1. You cannot specify a starting configuration of hidden nodes.
 2. All of the hidden layer transfer function are spherical with the same specified radius.
    Some generalizations that could be incorporated using the proximity to other classes
    a. Different radii 
    b. Different coordinate aligned ellipses

Hope this helps.

Thank you for formally accepting my answer

Greg

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MATLAB: When to use mapstd, mapminmax

I prefer standardization for 2 reasons

 1. Easier to detect outliers for removal or modification
 2. Typically, 0<= MSE <=1 so I use the MSE training goals 0.01 
(regression/pattern-recognition) and 0.005 (time-series).

HOWEVER,

 MATLAB uses MAPMINMAX as a default. Since it is too much a pain 
in the buttocks to replace it with MAPSTD, I just accept it but 
use ZSCORE standardization before training to check for outliers.

I have posted zillions of examples in both NEWSGROUP and ANSWERS.

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Code for regularization of RBF Neural Network

Try NEWFF with RADBAS, PURELIN and MSEREG.

Hope this helps.

Greg

Best Answer

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