Consecutive Identification of ANFIS-Based Fuzzy Systems with the Aid of Genetic Data Granulation

  • Sung-Kwun Oh
  • Keon-Jun Park
  • Witold Pedrycz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


We introduce a consecutive identification of ANFIS-based fuzzy systems with the aid of genetic data granulation to carry out the model identification of complex and nonlinear systems. The proposed model implements system structure and parameter identification with the aid of information granulation and genetic algorithms. The design methodology emerges as a hybrid structural optimization and parametric optimization. Information granulation realized with HCM clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions in the premise and the initial values of polynomial functions in the consequence. And the structure and the parameters of fuzzy model are identified by GAs and the membership parameters are tuned by GAs. In this case we exploit a consecutive identification. The numerical example is included to evaluate the performance of the proposed model.


Membership Function Fuzzy Model Recurrent Neural Network Chaotic Time Series Information Granulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sung-Kwun Oh
    • 1
  • Keon-Jun Park
    • 1
  • Witold Pedrycz
    • 2
    • 3
  1. 1.Department of Electrical EngineeringThe University of SuwonGyeonggi-doSouth Korea
  2. 2.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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