| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 387175 | Expert Systems with Applications | 2009 | 8 Pages |
Abstract
In this paper we propose a hybrid algorithm to optimize the structure of TSK type fuzzy model using backpropagation (BP) learning algorithm and non-dominated sorting genetic algorithm (NSGA-II). In a first step, BP algorithm is used to optimize the parameters of the model (parameters of membership functions and fuzzy rules). NSGA-II is used in a second phase, to optimize the number of fuzzy rules and to fine tune the parameters. A well known benchmark is used to evaluate performances of the proposed modelling approach, and compare it with other modelling approaches.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
O. Guenounou, A. Belmehdi, B. Dahhou,