Title :
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
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;
Conference_Titel :
Hybrid Intelligent Systems (HIS), 2013 13th International Conference on
Conference_Location :
Gammarth
Print_ISBN :
978-1-4799-2438-7
DOI :
10.1109/HIS.2013.6920457
