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A GA-based method for constructing fuzzy systems directly from numerical data.

C C Wong¹, C C Chen

¹Dept. of Electr. Eng., Tamkang Univ., Tamsui, Taiwan.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 7, 2008

Summary

This summary is machine-generated.

This study introduces a novel method combining genetic algorithms (GA) and recursive least-squares to build efficient fuzzy systems. The approach effectively identifies nonlinear systems using minimal rules while ensuring performance targets are met.

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Area of Science:

Computational Intelligence
Control Systems Engineering
Fuzzy Logic Systems

Background:

Fuzzy systems are widely used for modeling complex systems.
Designing effective fuzzy systems often requires significant expertise and computational resources.
Existing methods may struggle to balance system complexity with performance.

Purpose of the Study:

To propose a novel method for constructing fuzzy systems directly from input-output data.
To develop a system that automatically determines the optimal number of fuzzy rules and their parameters.
To ensure the constructed fuzzy system achieves a predetermined performance level.

Main Methods:

A hybrid approach integrating Genetic Algorithm (GA) for rule structure optimization and Recursive Least-Squares (RLS) for parameter tuning.
GA is employed to determine the number of fuzzy rules and their premise parts.
RLS is utilized to efficiently compute the consequent parts of the fuzzy rules.

Main Results:

The proposed method successfully constructs fuzzy systems with a reduced number of rules.
The method demonstrates effectiveness in approximating identified nonlinear systems.
Validation through three distinct nonlinear system identification problems confirms the approach's efficacy.

Conclusions:

The combined GA-RLS method offers an efficient strategy for data-driven fuzzy system construction.
This approach facilitates the development of parsimonious and high-performing fuzzy models.
The method provides a robust solution for identifying complex nonlinear systems.