NDLI: Selection of Radial Basis Functions via Genetic Algorithms in Pattern Recognition Problems

Content Provider	IEEE Xplore Digital Library
Author	Tinos, R. Murta, L.O.
Copyright Year	2008
Description	Author affiliation: Dept. de Fis. e Mat., FFCLRP - Univ. de Sao Paulo, Ribeirao (Tinos, R.; Murta, L.O.)
Abstract	The mixed use of different shapes of radial basis functions (RBFs) in RBF networks is investigated in this paper. For this purpose, we propose the use of the q-Gaussian function, which reproduces different RBFs by changing a real parameter q, in RBF networks. In the proposed methodology, the centers of the radial units are determined by the k-means algorithm. Then, a genetic algorithm is employed to select the number of hidden neurons, type and width of each RBF associated with each radial unit. In order to test the performance of the proposed methodology, an experimental study with two pattern recognition problems is presented. The RBF network with the q-Gaussian RBF is compared to RBF networks with Gaussian, Cauchy, and inverse multiquadratic RBFs.
Starting Page	171
Ending Page	176
File Size	183306
Page Count	6
File Format	PDF
ISBN	9781424432196
ISSN	15224899
DOI	10.1109/SBRN.2008.27
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-26
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Pattern recognition Radial basis function networks Neurons Shape Neural networks Testing Self organizing feature maps Evolutionary computation Search problems RBF Networks Radial basis function Genetic Algorithms q-Gaussian function Neural Networks
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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