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Fuzzy function approximators with ellipsoidal regions.

S Abe1

  • 1Graduate Sch. of Sci. & Technol., Kobe 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 two novel fuzzy function approximators, FACG and FALC, which dynamically generate fuzzy rules. These methods show promise in complex system modeling and data analysis.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Fuzzy Systems

Background:

  • Fuzzy function approximators are crucial for modeling complex systems.
  • Existing methods may lack dynamic rule generation capabilities.
  • Ellipsoidal regions offer advantages in fuzzy rule representation.

Purpose of the Study:

  • To introduce and evaluate two novel fuzzy function approximators: FACG and FALC.
  • To compare their performance against traditional methods like neural networks.
  • To assess their effectiveness in modeling complex data.

Main Methods:

  • FACG dynamically generates rules using Takagi-Sugeno models and center-of-gravity defuzzification.
  • FALC employs radial basis function networks and selects rules based on approximation error.

Related Experiment Videos

  • Both methods utilize least-squares for parameter determination with ellipsoidal regions.

    • Main Results:

    • Performance comparison of FACG and FALC against multilayered neural networks and other fuzzy approximators.
    • Evaluation using data from the Mackey-Glass differential equation.
    • Assessment on real-world data from a water purification plant.

    Conclusions:

    • FACG and FALC offer dynamic fuzzy rule generation with ellipsoidal regions.
    • The study provides a performance benchmark for these novel approximators.
    • Results indicate potential applications in complex system modeling and data analysis.