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

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  • Yu Huang
  • Feng Guo
  • Yongling Li
  • Yufeng Liu

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.

Suggested Citation

  • Yu Huang & Feng Guo & Yongling Li & Yufeng Liu, 2015. "Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm," PLOS ONE, Public Library of Science, vol. 10(1), pages 1-14, January.
    • Handle: RePEc:plo:pone00:0114910
    DOI: 10.1371/journal.pone.0114910
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    Cited by:

    1. Dong Liu & Zhihuai Xiao & Hongtao Li & Dong Liu & Xiao Hu & O.P. Malik, 2019. "Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization," Energies, MDPI, vol. 12(20), pages 1-21, October.

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