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Simultaneous structure identification and fuzzy rule generation for Takagi-Sugeno models.

Nikhil R Pal¹, Seemanti Saha

¹Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta 700108, India. nikhil@isical.ac.in

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

|November 22, 2008

Summary

This study introduces an integrated method for Takagi-Sugeno fuzzy systems to simultaneously identify fuzzy rules and select relevant features. This approach effectively handles high-dimensional data by considering nonlinear feature interactions, improving interpretability and efficiency.

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Area of Science:

Artificial Intelligence
Machine Learning
Fuzzy Logic Systems

Background:

Interpretability of fuzzy rule-based systems diminishes with high-dimensional data.
Identifying fuzzy rules and selecting relevant features are challenging in high-dimensional datasets.
Existing feature selection methods often overlook nonlinear feature interactions crucial for learning systems.

Purpose of the Study:

To propose an integrated method for Takagi-Sugeno fuzzy systems that simultaneously identifies fuzzy rules and selects features.
To address the challenge of structure identification in high-dimensional data by considering nonlinear feature interactions.
To develop a computationally attractive approach for feature selection and rule generation.

Main Methods:

An integrated learning mechanism is proposed to find irrelevant features and generate fuzzy rules concurrently.
The method accounts for nonlinear interactions between features and between features and the fuzzy rule-based system.
The approach is non-iterative, contrasting with traditional forward or backward selection methods.

Main Results:

The proposed method successfully identifies a small set of useful features and generates effective fuzzy rules.
It effectively handles subtle nonlinear interactions present in the data.
Demonstrated effectiveness on four well-studied function-approximation problems.

Conclusions:

The integrated method offers an efficient and effective solution for structure identification in Takagi-Sugeno fuzzy systems with high-dimensional data.
It enhances interpretability by selecting salient features and generating meaningful rules.
The non-iterative nature makes it computationally attractive for practical applications.