Design Methodology of Optimized IG_gHSOFPNN and Its Application to pH Neutralization Process

  • Ho-Sung Park
  • Kyung-Won Jang
  • Sung-Kwun Oh
  • Tae-Chon Ahn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


In this paper, we propose design methodology of optimized Information granulation based genetically optimized Hybrid Self-Organizing Fuzzy Polynomial Neural Networks (IG_gHSOFPNN) by evolutionary optimization. The augmented IG_gHSOFPNN results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HSOFPNN. The GA-based design procedure being applied at each layer of IG_gHSOFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HSOFPNN. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models.


Performance Index Fuzzy Rule Information Granulation Genetic Optimization Polynomial Neural Network 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ho-Sung Park
    • 1
  • Kyung-Won Jang
    • 1
  • Sung-Kwun Oh
    • 2
  • Tae-Chon Ahn
    • 1
  1. 1.School of Electrical Electronic and Information EngineeringWonkwang UniversityChon-BukSouth Korea
  2. 2.Department of Electrical EngineeringThe University of SuwonGyeonggi-doSouth Korea

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