Evolutionary Design of gdSOFPNN for Modeling and Prediction of NOx Emission Process

  • Tae-Chon Ahn
  • Ho-Sung Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


In this study, we proposed genetically dynamic optimized Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) with information granulation based Fuzzy Polynomial Neuron(FPN) (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structurally and parametrically optimized network through an optimal parameters design available within the FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.


Membership Function Fuzzy Rule Information Granulation Triangular Membership Function Gaussian Membership Function 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tae-Chon Ahn
    • 1
  • Ho-Sung Park
    • 1
  1. 1.Department of Electrical Electronic and Information EngineeringWonkwang UniversityChon-BukSouth Korea

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