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PLoS One. 2015 Jan 20;10(1):e0114910. doi: 10.1371/journal.pone.0114910. eCollection 2015.

Parameter estimation of fractional-order chaotic systems by using quantum parallel particle swarm optimization algorithm.

Author information

1
Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding, China; State Key Laboratory of Power Systems, Department of Thermal Engineering, Tsinghua University, Beijing, China.
2
Cognitive Science Department, School of Information Science and Engineering, Xiamen University, Xiamen, China.
3
Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding, China.
4
State Key Laboratory of Power Systems, Department of Thermal Engineering, Tsinghua University, Beijing, China.

Abstract

Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order 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.

PMID:
25603158
PMCID:
PMC4300217
DOI:
10.1371/journal.pone.0114910
[Indexed for MEDLINE]
Free PMC Article

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