Rule Extraction from Radial Basis Function Networks

Radial basis neural (RBF) networks provide an excellent solution to many pattern recognition and classication problems. However, RBF networks are also a local representation technique that enables the easy conversion of the hidden units into symbolic rules. This paper examines rules extracted from RBF networks. We use the iris ower classi- cation task and a vibration diagnosis classication task to illustrate the new knowledge extraction techniques. The rules are analyzed in order to gain knowledge and insight into the network representations. We argue that the local Gaussian representation in RBF networks is particularly useful for rule extraction.

@InProceedings{MTWM99, 
 	 author =  {McGarry, Ken and Tait, John and Wermter, Stefan and MacIntyre, J.},  
 	 title = {Rule Extraction from Radial Basis Function Networks}, 
 	 booktitle = {Proceedings of the International Conference on Artificial Neural Networks},
 	 number = {},
 	 volume = {},
 	 pages = {613--618},
 	 year = {1999},
 	 month = {Sep},
 	 publisher = {},
 	 doi = {}, 
 }