Hybrid learning fuzzy approach to function approximation

Chunshien Li, Tsunghan Wu, Tai Wei Chiang, Jhao Wun Hu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A new adaptive fuzzy approach to function approximation is proposed in the paper. A Takagi-Sugeno (T-S) type fuzzy system is used as the function approximator in the study. The proposed approach uses a hybrid learning method to train the T-S fuzzy system to achieve high accuracy in function approximation. The hybrid learning method combines both the particle swarm optimization (PSO) and the recursive least squares estimator (RLSE) to update the parameters of the fuzzy approximator. The PSO is used to update the premise part of the fuzzy system while the consequent part is updated by the RLSE. The PSO-RLSE learning method is very efficient in learning convergence. The proposed approach is compared to other methods. Three benchmark functions are used for the performance comparison. The proposed approach shows superior performance to compared approaches, in terms of approximation accuracy and learning convergence.

Original languageEnglish
Title of host publication2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010
DOIs
StatePublished - 2010
Event2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010 - Kuala Lumpur, Malaysia
Duration: 15 Jun 201017 Jun 2010

Publication series

Name2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010

Conference

Conference2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010
Country/TerritoryMalaysia
CityKuala Lumpur
Period15/06/1017/06/10

Keywords

  • Function approximation
  • Fuzzy
  • Machine learning
  • Particle swarm optimization (PSO)
  • Recursive least-squares estimator (RLSE)

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