Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm

被引：59
|
作者：
Huang, Yu [1 ,3 ]
Guo, Feng [2 ]
Li, Yongling [1 ]
Liu, Yufeng [3 ]
机构：
[1] North China Elect Power Univ, Hebei Engn Res Ctr Simulat & Optimized Control Po, Baoding, Peoples R China
[2] Xiamen Univ, Sch Informat Sci & Engn, Dept Cognit Sci, Xiamen, Peoples R China
[3] Tsinghua Univ, Dept Thermal Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China
来源：
PLOS ONE | 2015年 / 10卷 / 01期
关键词：
IDENTIFICATION; ATTRACTORS; DYNAMICS;
D O I：
10.1371/journal.pone.0114910
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Parameter estimation for fractional-order chaotic systems is an important issue in fractionalorder chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.
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收藏
页数：14
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