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An effective hybrid quantum-inspired evolutionary algorithm for parameter estimation of chaotic systems

机译:混沌系统参数估计的一种有效的混合量子启发式进化算法

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摘要

Parameter estimation of chaotic systems is an important issue and has attracted increasing interest from a variety of research fields. Recently, quantum-inspired evolutionary algorithms have been proposed and applied to some optimization problems. However, to the best of our knowledge, there is no published research work on quantum-inspired evolutionary algorithm (QEA) for estimating parameters of chaotic systems. In this paper, an effective hybrid quantum-inspired evolutionary algorithm with differential evolution (HQEDE) is proposed and applied to estimate the parameters of the Lorenz system. Numerical simulation and comparisons with other methods demonstrate the effectiveness and robustness of the proposed algorithm. In addition, the effects of the parameter settings on HQEDE are investigated.
机译:混沌系统的参数估计是一个重要的问题,并且引起了各个研究领域的越来越多的关注。最近,提出了量子启发式进化算法并将其应用于一些优化问题。然而,据我们所知,目前还没有关于量子启发式进化算法(QEA)的用于估计混沌系统参数的研究工作。提出了一种有效的具有差分演化的混合量子启发式进化算法(HQEDE),并将其应用于估计Lorenz系统的参数。数值仿真和与其他方法的比较证明了该算法的有效性和鲁棒性。此外,还研究了参数设置对HQEDE的影响。

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