Applications where Radial Basis and Probabilistic Neural Networks are successful respectively?
1 view (last 30 days)
Show older comments
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
Accepted Answer
Greg Heath
on 20 Feb 2014
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
2 Comments
Greg Heath
on 23 Feb 2014
As I said above,
1. RBFs are universal approximators.
2. I consider PNNs as a special case of RBFs.
3.I have no use for PNNs.
HTH
Greg
More Answers (0)
See Also
Categories
Find more on Define Shallow Neural Network Architectures in Help Center and File Exchange
Products
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!