Global optimization of radial basis function networks by hybrid simulated annealing

Jong Seok Lee, Cheol Hoon Park

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper presents a global optimization method of radial basis function networks. In the proposed method, stochastic search by simulated annealing is combined with a local search technique in order to perform global optimization of the network parameters with enhanced convergence speed. Its convergence property is proved mathematically. Experimental results demonstrate that the proposed method improves the performance of the networks over the conventional local and global training methods and reduces influence of the initial parameter values on the final results.

Original languageEnglish
Pages (from-to)519-537
Number of pages19
JournalNeural Network World
Volume20
Issue number4
Publication statusPublished - 2010 Sep 22

Fingerprint

Radial basis function networks
Global optimization
Simulated annealing
Local search (optimization)

All Science Journal Classification (ASJC) codes

  • Software
  • Neuroscience(all)
  • Hardware and Architecture
  • Artificial Intelligence

Cite this

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Global optimization of radial basis function networks by hybrid simulated annealing. / Lee, Jong Seok; Park, Cheol Hoon.

In: Neural Network World, Vol. 20, No. 4, 22.09.2010, p. 519-537.

Research output: Contribution to journalArticle

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