Training ANFIS parameters with a quantum-behaved particle swarm optimization algorithm

Authors:
Xiufang Lin;Jun Sun;Vasile Palade;Wei Fang;Xiaojun Wu;Wenbo Xu
Affiliations:
Key Laboratory of Advanced Control for Light Industry (Ministry of China), Wuxi, Jiangsu, China;Key Laboratory of Advanced Control for Light Industry (Ministry of China), Wuxi, Jiangsu, China;Department of Computer Science, University of Oxford, Oxford, United Kingdom;Key Laboratory of Advanced Control for Light Industry (Ministry of China), Wuxi, Jiangsu, China;Key Laboratory of Advanced Control for Light Industry (Ministry of China), Wuxi, Jiangsu, China;Key Laboratory of Advanced Control for Light Industry (Ministry of China), Wuxi, Jiangsu, China
Venue:
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2012

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Abstract

This paper proposes a novel method for training the parameters of an adaptive network based fuzzy inference system (ANFIS). Different from previous approaches, which emphasized on the use of gradient descent (GD) methods, we employ a method based on. Quantum-behaved Particle Swarm Optimization (QPSO) for training the parameters of an ANFIS. The ANFIS trained by the proposed method is applied to nonlinear system modeling and chaotic prediction. The simulation results show that the ANFIS-QPSO method performs much better than the original ANFIS and the ANFIS-PSO method.