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Training ANFIS Parameters with a Quantum-behaved Particle Swarm Optimization Algorithm

  • Xiufang Lin
  • Jun Sun
  • Vasile Palade
  • Wei Fang
  • Xiaojun Wu
  • Wenbo Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

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.

Keywords

Particle swarm optimization quantum-behaved particle swarm Optimization training algorithm evolutionary fuzzy systems 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiufang Lin
    • 1
  • Jun Sun
    • 1
  • Vasile Palade
    • 2
  • Wei Fang
    • 1
  • Xiaojun Wu
    • 1
  • Wenbo Xu
    • 1
  1. 1.Key Laboratory of Advanced Control for Light Industry (Ministry of China)WuxiChina
  2. 2.Department of Computer ScienceUniversity of OxfordOxfordUnited Kingdom

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