Multi-objective optimization of TSK fuzzy models

被引：15
|
作者：
Guenounou, O. [1 ]
Belmehdi, A. [1 ]
Dahhou, B. [2 ]
机构：
[1] Univ Bejaia, Lab Ind Technol & Informat LT21, Bejaia, Algeria
[2] CNRS, LAAS, F-31077 Toulouse, France
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 04期
关键词：
Backpropagation; Genetic algorithms/NSGA-II; Fuzzy rules; Hybrid algorithm; Structure; GENETIC ALGORITHMS; DYNAMIC-SYSTEMS; IDENTIFICATION;
D O I：
10.1016/j.eswa.2008.09.044
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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). NSCA-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. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码：7416 / 7423
页数：8
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