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Learning fuzzy inference systems using an adaptive membership function scheme.

A Lotfi1, A C Tsoi

  • 1Dept. of Electr. & Comput. Eng., Queensland Univ., Brisbane, Qld.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1996
PubMed
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This study introduces an adaptive fuzzy system that automatically adjusts membership functions for nonlinear tasks. This approach minimizes rules and enhances control system performance.

Area of Science:

  • Fuzzy Logic Systems
  • Control Theory
  • Machine Learning

Background:

  • General additive fuzzy systems often require precise, predefined membership functions.
  • Adapting these functions for complex nonlinear problems can be challenging and rule-intensive.

Purpose of the Study:

  • To propose an adaptive membership function scheme for general additive fuzzy systems.
  • To enable fuzzy systems to handle arbitrary nonlinear input-output mappings with minimal rules.

Main Methods:

  • Developed an adaptive scheme to automatically adjust membership functions.
  • Utilized error computation between actual and desired decision surfaces for parameter adjustment.
  • Employed nonlinear function approximation and truck backer-upper control for validation.

Related Experiment Videos

Main Results:

  • The proposed scheme successfully adapts membership functions for nonlinear mappings.
  • Demonstrated minimization of the number of rules required for fuzzy systems.
  • Validated the method's viability through practical control system applications.

Conclusions:

  • The adaptive membership function scheme offers an efficient approach for general additive fuzzy systems.
  • This method reduces complexity and improves adaptability for nonlinear control tasks.