Multi groups cooperation based symbiotic evolution for TSK-type neuro-fuzzy systems design

Yung Chi Hsu, Sheng-Fuu Lin*, Yi Chang Cheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In this paper, a TSK-type neuro-fuzzy system with multi groups cooperation based symbiotic evolution method (TNFS-MGCSE) is proposed. The TNFS-MGCSE is developed from symbiotic evolution. The symbiotic evolution is different from traditional GAs (genetic algorithms) that each chromosome in symbiotic evolution represents a rule of fuzzy model. The MGCSE is different from the traditional symbiotic evolution; with a population in MGCSE is divided into several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperate with other groups to generate the better chromosomes by using the proposed cooperation based crossover strategy (CCS). In this paper, the proposed TNFS-MGCSE is used to evaluate by numerical examples (Mackey-Glass chaotic time series and sunspot number forecasting). The performance of the TNFS-MGCSE achieves excellently with other existing models in the simulations.

Original languageEnglish
Pages (from-to)5320-5330
Number of pages11
JournalExpert Systems with Applications
Volume37
Issue number7
DOIs
StatePublished - 1 Jul 2010

Keywords

  • Chaotic time series
  • Genetic algorithms
  • Neural fuzzy system
  • Symbiotic evolution

Fingerprint Dive into the research topics of 'Multi groups cooperation based symbiotic evolution for TSK-type neuro-fuzzy systems design'. Together they form a unique fingerprint.

Cite this