Efficient function approximation using an online self-regulating clustering algorithm

Jiun Kai Wang, Jeen-Shing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an online self-regulating clustering algorithm (SRCA) to construct parsimonious radial basis function networks (RBFN) for function approximation applications. Growing, merging and splitting mechanisms with online operation capability are integrated into the proposed SRCA. These mechanisms enable the SRCA to identify a suitable cluster configuration without a priori knowledge regarding the approximation problems. In addition, a novel idea for cluster boundary estimation has been proposed to effectively maintain the resultant clusters with compact hyper-elliptic-shaped boundaries. Computer simulations show that RBFN constructed by the SRCA can approximate functions with a high accuracy and fast learning convergence. Benchmark examples and comparisons with some; existing approaches have been conducted to validate the effectiveness and feasibility of the SRCA for function approximation problems.

Original languageEnglish
Title of host publication2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Pages5935-5940
Number of pages6
DOIs
Publication statusPublished - 2004 Dec 1
Event2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 - The Hague, Netherlands
Duration: 2004 Oct 102004 Oct 13

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume6
ISSN (Print)1062-922X

Other

Other2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
CountryNetherlands
CityThe Hague
Period04-10-1004-10-13

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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