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
查看参考文献15篇
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文摘
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为了对分数阶超混沌系统中的未知参数进行准确估计,提出一种量子混沌粒子群优化算法(Quantum chaos particle swarm optimization,QCPSO).该算法通过对量子粒子群优化算法(Quantum behaved particle swarm optimization,QPSO)的实现机理进行分析,并结合量子纠缠与混沌系统之间的相关性而实现.首先,将量子势阱中心视为混沌吸引子围绕的不动点,处于吸引子外部的粒子会逐渐聚集于吸引子之内,而处于吸引子内部的粒子会出现快速分离扩散的现象;然后,采用基于随机映射的粒子更新机制,充分保证混沌粒子的初值多样性;最后,提出了基于不动点中心的尺度自适应策略,解决了算法后期的搜索停滞问题.运用QCPSO算法对典型分数阶超混沌系统参数进行估计,结果表明,该算法在收敛速度与精度上优于改进的差分进化算法、自适应人工蜂群算法以及改进的量子粒子群优化算法. |
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其他语种文摘
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A new quantum chaos particle swarm optimization (QCPSO) was proposed to accurately estimate the uncertain parameters of the fractional order hyper chaotic system.The QCPSO algorithm was realized by analyzing the mechanism of quantum behaved particle swarm optimization (QPSO) and combining the correlation between quantum entanglement 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 mechanism 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 algorithm,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. |
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来源
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电子学报
,2018,46(2):333-340 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2018.02.011
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关键词
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量子粒子群优化算法
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混沌映射
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混沌吸引子
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分数阶超混沌系统
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地址
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1.
山西大学计算机与信息技术学院, 山西, 太原, 030006
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山西大学大数据科学与产业研究院, 山西, 太原, 030006
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中国科学院广州地球化学研究所, 广东, 广州, 510640
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中国科学院大学, 北京, 100049
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中国科学院广州电子技术研究所, 广东, 广州, 510070
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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中国科学院西部之光人才培养计划
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文献收藏号
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CSCD:6197910
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参考文献 共
15
共1页
|
|
1.
Lorenz E N. Atmospheric predictability as revealed by naturally occurring analogues.
Journal of Atmospheric Sciences,1969,26(1969):636-646
|
CSCD被引
66
次
|
|
|
|
|
2.
Lassoued A. On new chaotic and hyperchaotic systems: A literature survey.
Nonlinear Analysis Modelling and Control,2017,21(6):770-789
|
CSCD被引
1
次
|
|
|
|
|
3.
Wu X. Generalized projective synchronization of the fractional-order Chen hyperchaotic system.
Nonlinear Dynamics,2009,57(1/2):25-35
|
CSCD被引
2
次
|
|
|
|
|
4.
任开军. 自适应人工蜂群优化的混沌系统参数估计.
国防科技大学学报,2015(5):135-140
|
CSCD被引
6
次
|
|
|
|
|
5.
何廷年. 改进多种群差分进化算法的混沌系统参数估计.
计算机工程,2015,41(2):178-183
|
CSCD被引
2
次
|
|
|
|
|
6.
黄宇. 基于量子并行粒子群优化算法的分数阶混沌系统参数估计.
物理学报,2015,64(3):228-235
|
CSCD被引
5
次
|
|
|
|
|
7.
Kennedy J. Particle swarm optimization.
Proceedings of IEEE International Conference on Neural Networks,1995:1942-1948
|
CSCD被引
765
次
|
|
|
|
|
8.
李祚泳. PSO算法优化BP网络的新方法及仿真实验.
电子学报,2008,36(11):2224-2228
|
CSCD被引
21
次
|
|
|
|
|
9.
Wang J. A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO Algorithms.
Chinese Journal of Electronics,2016,25(2):320-327
|
CSCD被引
9
次
|
|
|
|
|
10.
李奕. 粒子群进化学习自适应双通道脉冲耦合神经网络图像融合方法研究.
电子学报,2014,42(2):217-222
|
CSCD被引
10
次
|
|
|
|
|
11.
Frans V D B.
An analysis of particle swarm optimizers,2002
|
CSCD被引
8
次
|
|
|
|
|
12.
Sun J. Particle swarm optimization with particles having quantum behavior.
Processing of IEEE Congress on Evolutionary Computation,2004:325-331
|
CSCD被引
1
次
|
|
|
|
|
13.
Xi Maolong. An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position.
Applied Mathematics and Computation,2008,205(2):751-759
|
CSCD被引
16
次
|
|
|
|
|
14.
赵吉. 基于演化历史信息的自变异协同量子行为粒子群优化算法.
电子学报,2016,44(12):2900-2907
|
CSCD被引
2
次
|
|
|
|
|
15.
Yuan X. Parallel chaos optimization algorithm with migration and merging operation.
Applied Soft Computing,2015,35(C):591-604
|
CSCD被引
2
次
|
|
|
|
|
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