NDLI: Quantum genetic algorithm and its application to designing fuzzy neural controller

Content Provider	IEEE Xplore Digital Library
Author	Panchi Li Kaoping Song Erlong Yang
Copyright Year	2010
Description	Author affiliation: School of Computer & Information Technology, Daqing Petroleum Institute, Heilongjiang, China (Panchi Li) \|\| Post-Doctoral Research Center of Oil & Gas Engineering, Daqing Petroleum Institute, Heilongjiang, China (Kaoping Song; Erlong Yang)
Abstract	Aiming at the BP algorithm convergence difficulty for fuzzy neural controller with many parameters, a quantum genetic algorithm is proposed to optimize the parameters of a normalized fuzzy neural controller. In the proposed method, Chromosomes are comprised of qubits, and updated by quantum rotation gates, mutated by quantum non-gates. The probability amplitudes of each qubit are regarded as two coordinate genes, each chromosome contains two gene chains, and each gene chain represents an optimization solution, which can accelerate the convergence process and increase the successful probability. First, the parameters of the normalized fuzzy neural controller are encoded into an individual, and the initial colony is composed of some random individuals, then the global searching is performed by the proposed algorithm. Finally, the designed controller is employed to control an inverted pendulum system, and the simulation results verified the effectiveness of the proposed algorithm.
Starting Page	2994
Ending Page	2998
File Size	243333
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5582462
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis fuzzy control Fuzzy control fuzzy neural network quantum genetic algorithm Artificial neural networks Logic gates Fuzzy neural networks parameter optimization Optimization Biological cells
Content Type	Text
Resource Type	Article

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