Fuzzy modeling system based on hybrid evolutionary approach

Author

Jarraya, Yosr ; Bouaziz, Souhir ; Alimi, Adel M. ; Abraham, Ajith

Author_Institution

Res. Group on Intell. Machines (REGIM), Univ. of Sfax, Sfax, Tunisia

fYear

2013

Firstpage

72

Lastpage

77

Abstract

In this paper, we introduce a new evolutionary methodology to design fuzzy inference systems. An innovative hybrid stages of learning method and tuning method, contains Subtractive clustering, Adaptive Neuro-Fuzzy Inference System (ANFIS) and particle swarm optimization (PSO), is developed to generate evolutional fuzzy modeling systems with high accuracy. For the purpose of illustration and validation of the approach, some data sets have been exploited. Empirical results illustrate that the proposed method is efficient.

Keywords

evolutionary computation; fuzzy neural nets; fuzzy reasoning; fuzzy systems; identification; learning (artificial intelligence); modelling; particle swarm optimisation; pattern clustering; ANFIS; PSO; adaptive neuro-fuzzy inference system; fuzzy model identification problem; fuzzy modeling system; hybrid evolutionary approach; learning method; particle swarm optimization; subtractive clustering; tuning method; Accuracy; Classification algorithms; Computational modeling; Engines; Optimization; Search problems; Training; Adaptive Neuro-Fuzzy; Fuzzy Membership function; Fuzzy models; Subtractive clustering; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems (HIS), 2013 13th International Conference on

Conference_Location

Gammarth

Print_ISBN

978-1-4799-2438-7

Type

conf

DOI

10.1109/HIS.2013.6920457

Filename

6920457