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Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm
Joint Authors
Liu, Peilin
Leng, Wenhao
Fang, Wei
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-06-18
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This paper proposes a novel method of training the parameters of adaptive-network-based fuzzy inference system (ANFIS).
Different from the previous works which emphasized on gradient descent (GD) method, we present an approach to train the parameters of ANFIS by using an improved version of quantum-behaved particle swarm optimization (QPSO).
This novel variant of QPSO employs an adaptive dynamical controlling method for the contraction-expansion (CE) coefficient which is the most influential algorithmic parameter for the performance of the QPSO algorithm.
The ANFIS trained by the proposed QPSO with adaptive dynamical CE coefficient (QPSO-ADCEC) is applied to five example systems.
The simulation results show that the ANFIS-QPSO-ADCEC method performs much better than the original ANFIS, ANFIS-PSO, and ANFIS-QPSO methods.
American Psychological Association (APA)
Liu, Peilin& Leng, Wenhao& Fang, Wei. 2013. Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1010008
Modern Language Association (MLA)
Liu, Peilin…[et al.]. Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm. Mathematical Problems in Engineering No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-1010008
American Medical Association (AMA)
Liu, Peilin& Leng, Wenhao& Fang, Wei. Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1010008
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1010008