Complex fuzzy model with PSO-RLSE hybrid learning approach to function approximation

Chunshien Li, Tai Wei Chiang

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


A new neuro-fuzzy computing paradigm using complex fuzzy sets to the problem of function approximation is proposed in this paper. The concept of complex fuzzy sets is an extension of traditional fuzzy set whose membership degrees are within a unit disc in the complex plane. The proposed complex system has excellent input-output mapping ability. To update the free parameters of the proposed complex neuro-fuzzy system (CNFS), a novel hybrid learning method is devised, combining both the well-known particle swarm optimisation (PSO) algorithm and the recursive least squares estimator (RLSE) algorithm. By the PSO-RLSE hybrid learning method, fast learning convergence is observed and better performance in accuracy is shown. To test the proposed approach, two benchmark functions are used. The experimental results by the proposed approach are compared to its neuro-fuzzy counterpart and to other approaches in literature. According to the experiment results, excellent performance by the proposed approach has been exposed.

Original languageEnglish
Pages (from-to)409-430
Number of pages22
JournalInternational Journal of Intelligent Information and Database Systems
Issue number4
StatePublished - Jul 2011


  • CFS
  • CNFS
  • Complex fuzzy set
  • Complex neuro-fuzzy system
  • Function approximation
  • PSO
  • Particle swarm optimisation
  • RLSE
  • Recursive least squares estimator


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