Optimal design of normalized fuzzy neural network controller based on quantum genetic algorithm

被引：0
|
作者：
Li, Pan-Chi
Li, Shi-Yong
机构：
[1] Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
[2] School of Computer and Information Technology, Daqing Petroleum Institute, Daqing 163318, China
来源：
Xitong Fangzhen Xuebao / Journal of System Simulation | 2007年 / 19卷 / 16期
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摘要：
A quantum genetic algorithm was proposed to design the parameters of a normalized fuzzy neural network controller. In this method, chromosomes are comprised of quantum bits, and are updated by quantum rotation gate, and are mutated by quantum non-gate. The probability amplitudes of each quantum bit are regarded as two paratactic genes, each chromosome contains two chains of genes, and each chain of genes represents an optimization result, which can accelerate convergence process and increase successful probability for the same number of chromosomes. The parameters of the normalized fuzzy neural network controller were encoded into some individuals, and the initial colony was formed by some random individuals. A global searching was performed by quantum genetic algorithm. The simulation results show the effectiveness of this method.
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页码：3710 / 3714
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