FSPN-Based Genetically Optimized Fuzzy Polynomial Neural Networks

  • Sung-Kwun Oh
  • Seok-Beom Roh
  • Daehee Park
  • Yong-Kab Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3483)


In this paper, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy set-based polynomial neurons (FSPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.


Fuzzy Rule Triangular Membership Function Elitist Strategy Genetically Optimize Genetic Fuzzy System 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sung-Kwun Oh
    • 1
  • Seok-Beom Roh
    • 2
  • Daehee Park
    • 2
  • Yong-Kab Kim
    • 2
  1. 1.Department of Electrical EngineeringThe University of SuwonHwaseong-si, Gyeonggi-doSouth Korea
  2. 2.Department of Electrical Electronic and Information EngineeringWonkwang UniversityChon-BukSouth Korea

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