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IJMLC 2014 Vol. 4(5): 433-436 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.450

Symbiotic Particle Swarm Optimization for Neural Fuzzy Controllers

Cheng-Hung Chen and Wen-Hsien Chen

Abstract—This study proposes a symbiotic particle swarm optimization (SPSO) for the specific neural fuzzy controller (NFC). The specific NFC model using compensatory fuzzy operators of neural fuzzy networks makes fuzzy logic systems more adaptive and effective. The proposed SPSO adopts a multiple swarm scheme that uses each particle represents a single fuzzy rule and each particle in each swarm evolves separately to avoid falling in a local optimal solution. Furthermore, the SPSO embeds the symbiotic evolution scheme in a specific particle swarm optimization (PSO) to accelerate the search and increase global search capacity.

Index Terms—Water bath temperature system, neural fuzzy networks, symbiotic evolution, particle swarm optimization.

C. H. Chen and W. H. Chen are with the Department of Electrical Engineering, National Formosa University, Yunlin County, Taiwan, ROC (e-mail: chchen.ee@nfu.edu.tw).

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Cite: Cheng-Hung Chen and Wen-Hsien Chen, "Symbiotic Particle Swarm Optimization for Neural Fuzzy Controllers," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 433-436, 2014.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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