Nonlinear System Control Using Functional-Link-Based Neuro-Fuzzy Network Model Embedded with Modified Particle Swarm Optimizer

Miin-Tsair Su, Chin-Teng Lin, Sheng-Chih Hsu, Dong Lin Li, Cheng Jian Lin, Cheng-Hung Chen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This study presents an evolutionary neural fuzzy system (NFS) for nonlinear system control. The proposed NFS model uses functional link neural networks (FLNNs) as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. A learning algorithm, which consists of structure learning and parameter learning, is presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the particle swarm optimization (PSO) algorithm, can adjust the shape of the membership function and the corresponding weighting of the FLNN. The distance-based mutation operator, which strongly encourages a global search giving the particles more chance of converging to the global optimum, is introduced. The simulation results have shown the proposed method can improve the searching ability and is very suitable for the nonlinear system control applications.
Original languageEnglish
Pages (from-to)97-109
Number of pages13
JournalInternational Journal of Fuzzy Systems
Volume14
Issue number1
DOIs
StatePublished - Mar 2012

Keywords

  • Functional link neural networks (FLNNs); mutation operator; neuro-fuzzy networks (NFNs); particle swarm optimization (PSO); perturbation operator
  • SYMBIOTIC EVOLUTION; LOGIC CONTROLLER; DESIGN; CLASSIFICATION; ALGORITHM; MUTATION; PSO

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