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

Xiufang Lin, Jun Sun, Vasile Palade, Wei Fang, Xiaojun Wu, Wenbo Xu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

9 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence
Subtitle of host publicationThird International Conference, ICSI 2012, Proceedings
EditorsYing Tan, Yuhui Shi, Zhen Ji
Place of PublicationBerlin
PublisherSpringer
Pages148-155
Number of pages8
Volume7331 LNCS
EditionPART 1
ISBN (Electronic)9783642309762
ISBN (Print)9783642309755
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event3rd International Conference on Swarm Intelligence, ICSI 2012 - Shenzhen, China
Duration: 17 Jun 201220 Jun 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7331 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference3rd International Conference on Swarm Intelligence, ICSI 2012
CountryChina
CityShenzhen
Period17/06/1220/06/12

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Training ANFIS parameters with a quantum-behaved particle swarm optimization algorithm'. Together they form a unique fingerprint.

Cite this