Adaptive fuzzy approach to function approximation with PSO and RLSE

Chunshien Li, Tsunghan Wu

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


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
Pages (from-to)13266-13273
Number of pages8
JournalExpert Systems with Applications
Issue number10
StatePublished - 15 Sep 2011


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


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