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

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

doi:10.1007/978-3-642-30976-2_18

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 proceeding › Conference proceeding › peer-review

12 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 language	English
Title of host publication	Advances in Swarm Intelligence
Subtitle of host publication	Third International Conference, ICSI 2012, Proceedings
Editors	Ying Tan, Yuhui Shi, Zhen Ji
Place of Publication	Berlin
Publisher	Springer
Pages	148-155
Number of pages	8
Volume	7331 LNCS
Edition	PART 1
ISBN (Electronic)	9783642309762
ISBN (Print)	9783642309755
DOIs	https://doi.org/10.1007/978-3-642-30976-2_18
Publication status	Published - 2012
Externally published	Yes
Event	3rd International Conference on Swarm Intelligence, ICSI 2012 - Shenzhen, China Duration: 17 Jun 2012 → 20 Jun 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7331 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	3rd International Conference on Swarm Intelligence, ICSI 2012
Country/Territory	China
City	Shenzhen
Period	17/06/12 → 20/06/12

Keywords

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

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

Access to Document

10.1007/978-3-642-30976-2_18

Cite this

Lin, X., Sun, J., Palade, V., Fang, W., Wu, X., & Xu, W. (2012). Training ANFIS parameters with a quantum-behaved particle swarm optimization algorithm. In Y. Tan, Y. Shi, & Z. Ji (Eds.), Advances in Swarm Intelligence : Third International Conference, ICSI 2012, Proceedings (PART 1 ed., Vol. 7331 LNCS, pp. 148-155). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7331 LNCS, No. PART 1). Springer. https://doi.org/10.1007/978-3-642-30976-2_18

Training ANFIS parameters with a quantum-behaved particle swarm optimization algorithm. / Lin, Xiufang; Sun, Jun; Palade, Vasile et al.
Advances in Swarm Intelligence : Third International Conference, ICSI 2012, Proceedings. ed. / Ying Tan; Yuhui Shi; Zhen Ji. Vol. 7331 LNCS PART 1. ed. Berlin: Springer, 2012. p. 148-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7331 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Lin, X, Sun, J, Palade, V, Fang, W, Wu, X & Xu, W 2012, Training ANFIS parameters with a quantum-behaved particle swarm optimization algorithm. in Y Tan, Y Shi & Z Ji (eds), Advances in Swarm Intelligence : Third International Conference, ICSI 2012, Proceedings. PART 1 edn, vol. 7331 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7331 LNCS, Springer, Berlin, pp. 148-155, 3rd International Conference on Swarm Intelligence, ICSI 2012, Shenzhen, China, 17/06/12. https://doi.org/10.1007/978-3-642-30976-2_18

Lin X, Sun J, Palade V, Fang W, Wu X, Xu W. Training ANFIS parameters with a quantum-behaved particle swarm optimization algorithm. In Tan Y, Shi Y, Ji Z, editors, Advances in Swarm Intelligence : Third International Conference, ICSI 2012, Proceedings. PART 1 ed. Vol. 7331 LNCS. Berlin: Springer. 2012. p. 148-155. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-30976-2_18

Lin, Xiufang ; Sun, Jun ; Palade, Vasile et al. / Training ANFIS parameters with a quantum-behaved particle swarm optimization algorithm. Advances in Swarm Intelligence : Third International Conference, ICSI 2012, Proceedings. editor / Ying Tan ; Yuhui Shi ; Zhen Ji. Vol. 7331 LNCS PART 1. ed. Berlin : Springer, 2012. pp. 148-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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AB - 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.

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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