Multi-objective optimization of TSK fuzzy models

Author

Guenounou, Ouahib ; Belmehdi, Ali ; Dahhou, Boutaieb

Author_Institution

Lab. of Ind. Technol. & Inf., Univ. of Bejaia, Bejaia

fYear

2008

fDate

20-22 July 2008

Firstpage

1

Lastpage

6

Abstract

In this paper we propose a hybrid algorithm to optimize the structure of TSK type fuzzy model using back-propagation (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 modeling approach, and compare it with other modeling approaches.

Keywords

backpropagation; fuzzy reasoning; fuzzy set theory; genetic algorithms; sorting; NSGA-II; TSK fuzzy models; back-propagation learning algorithm; fuzzy membership functions; fuzzy rules; multiobjective optimization; nondominated sorting genetic algorithm; parameter tuning; Backpropagation algorithms; Control system synthesis; Electronic mail; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Hybrid intelligent systems; Neural networks; Sorting; Genetic algorithms/NSGA-II; back-propagation; fuzzy rules; hybrid algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Signals and Devices, 2008. IEEE SSD 2008. 5th International Multi-Conference on

Conference_Location

Amman

Print_ISBN

978-1-4244-2205-0

Electronic_ISBN

978-1-4244-2206-7

Type

conf

DOI

10.1109/SSD.2008.4632782

Filename

4632782