AN SOM-based algorithm for optimization with dynamic weight updating

Y. I.Yuan Chen, Kuu-Young Young*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The self-organizing map (SOM), as a kind of unsupervised neural network, has been used for both static data management and dynamic data analysis. To further exploit its search abilities, in this paper we propose an SOM-based algorithm (SOMS) for optimization problems involving both static and dynamic functions. Furthermore, a new SOM weight updating rule is proposed to enhance the learning efficiency; this may dynamically adjust the neighborhood function for the SOM in learning system parameters. As a demonstration, the proposed SOMS is applied to function optimization and also dynamic trajectory prediction, and its performance compared with that of the genetic algorithm (GA) due to the similar ways both methods conduct searches.

Original languageEnglish
Pages (from-to)171-181
Number of pages11
JournalInternational journal of neural systems
Volume17
Issue number3
DOIs
StatePublished - 1 Jun 2007

Keywords

  • Dynamic function
  • Genetic algorithm
  • Optimization
  • Self-organizing map

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