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


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
Issue number3
StatePublished - 1 Jun 2007


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


Dive into the research topics of 'AN SOM-based algorithm for optimization with dynamic weight updating'. Together they form a unique fingerprint.

Cite this