电子学报2018，Vol.46Issue(2)：333-340,8.DOI:10.3969/j.issn.0372-2112.2018.02.011
基于量子混沌粒子群优化算法的分数阶超混沌系统参数估计
New Quantum Chaos Particle Swarm Optimization Algorithm for Estimating the Parameter of Fractional Order Hyper Chaotic System
摘要
Abstract
A new quantum chaos particle swarm optimization(QCPSO) was proposed to accurately estimate the un-certain parameters of the fractional order hyper chaotic system.The QCPSO algorithm was realized by analyzing the mecha-nism of quantum behaved particle swarm optimization (QPSO) and combining the correlation between quantum entangle-ment and chaotic system.Firstly,the center of potential well was replaced by a fixed point of chaotic attractor.The particles which outside the attractor were gradually converged to the attractor,and the particles which inside the attractor were quickly diffused.Secondly,in order to guarantee the diversity of the initial value of the chaotic particles,the particle update mecha-nism based on random mapping was proposed.Finally,a scale adaptive strategy was proposed to solve the problem of search stagnation of the algorithm.The parameters of fractional order hyper chaotic system were estimated by the QCPSO algo-rithm,and the results showed that the QCPSO algorithm has faster convergence speed and higher accuracy than improved differential evolution algorithm,adaptive artificial bee colony algorithm and improved QPSO algorithm.关键词
量子粒子群优化算法/混沌映射/混沌吸引子/分数阶超混沌系统Key words
quantum behaved particle swarm optimization/chaotic maps/strange attractor/fractional order hyper cha-otic system分类
信息技术与安全科学引用本文复制引用
闫涛,刘凤娴,陈斌..基于量子混沌粒子群优化算法的分数阶超混沌系统参数估计[J].电子学报,2018,46(2):333-340,8.基金项目
中国科学院西部之光基金(No.2011180) （No.2011180）
