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A systematic approach to a self-generating fuzzy rule-table for function approximation.

H Pomares1, I Rojas, J Ortega

  • 1Dept. de Arquitectura y Tecnologia de Computadores, Granada Univ.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2008
PubMed
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This study introduces a systematic design for automatically creating fuzzy systems. The method optimizes fuzzy rules, membership functions, and system structure for improved function approximation.

Area of Science:

  • Computational intelligence
  • Machine learning
  • Fuzzy logic systems

Background:

  • Fuzzy systems require manual design for structure and parameters.
  • Automating fuzzy system design is crucial for complex modeling tasks.
  • Existing methods often struggle with simultaneous structure and parameter identification.

Purpose of the Study:

  • To propose a systematic and automated design for fuzzy system structure and parameter learning.
  • To address the optimization of fuzzy rule parameters and system structure identification.
  • To develop a method for automatic fuzzy system construction from training data.

Main Methods:

  • A four-step systematic design approach is presented.
  • Step 1: Optimizes fuzzy rules for a given membership function configuration.

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  • Steps 2-4: Iteratively optimize membership functions, rule conclusions, system topology, and select the best structure based on approximation error.

    • Main Results:

    • The proposed method successfully builds fuzzy systems automatically.
    • Demonstrates effective function approximation capabilities.
    • Achieves improved performance compared to existing methodologies in the literature.

    Conclusions:

    • The systematic design provides an effective automated approach for fuzzy system modeling.
    • The method offers a robust solution for optimizing both fuzzy system structure and parameters.
    • This work contributes to advancing automated fuzzy system design for function approximation.