| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 720909 | IFAC Proceedings Volumes | 2007 | 6 Pages |
Abstract
This paper presents an approach for online Takagi-Sugeno fuzzy models generation, which can be applied for nonlinear systems identification. The algorithm combines a proposed on-line clustering technique with least squares methods. Both the structure and parameters of the fuzzy system are updated on line. The new clustering method for the structure identification can divide input-output data into different groups (rules) using on line data. After the rules are determined, the consequent parameters are tuned by using least squares estimators.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Boris Martínez, Jesús Fernández, Erick Marichal, Francisco Herrera,