A new approach to fuzzy modeling

This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model (1985), because it has the same structure as that of Takagi and Sugeno's model. It is also as easy to implement as...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 5; no. 3; pp. 328 - 337
Main Authors: Kim, Euntai, Park, Minkee, Ji, Seunghwan, Park, Mignon
Format: Journal Article
Language:English
Published: IEEE 01.08.1997
Subjects:
Application software
Clustering algorithms
Equations
Fuzzy sets
Fuzzy systems
Humans
Microwave integrated circuits
Optimization methods
Pattern recognition
Sections
ISSN:1063-6706
Online Access:Get full text
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Summary:This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model (1985), because it has the same structure as that of Takagi and Sugeno's model. It is also as easy to implement as Sugeno and Yasukawa's model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawa's model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm.
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ISSN:1063-6706
DOI:10.1109/91.618271