NDLI: Growing radial basis neural networks with potential function generators

Content Provider	IEEE Xplore Digital Library
Author	Valova, I. Georgiev, G. Gueorguieva, N.
Copyright Year	2005
Description	Author affiliation: Comput. Sci., Massachusetts Univ., Dartmouth, MA, USA (Valova, I.)
Abstract	In this paper, we propose an approach for shaping the adaptive radial basis functions through potential functions for the purposes of classification. We propose a multilayer potential function generators neural network (PFUGNN) with two fundamental components: potential function generators (PFGs) and potential function entities (PFEs) which create the decision rules by constructing multivariate potential functions and adjusting the weights as well as the parameters of the cumulative potential functions. The two proposed criteria evaluate the NN performance during the learning phase and force PFUGNN to enter the dynamic phase and perform structural changes before entering the next learning cycle. The implementation of the presented method with several data sets demonstrates its power in generating classification solutions for learning samples of various shapes.
Starting Page	779
Ending Page	784
File Size	3444496
Page Count	6
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571241
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Signal generators Computer science Multi-layer neural network Radial basis function networks Interpolation Surface fitting Performance evaluation Power generation Shape neural networks Potential function generators radial basis functions
Content Type	Text
Resource Type	Article

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