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An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation

机译:混沌系统参数估计问题的一种改进的布谷鸟搜索优化算法

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摘要

This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior.
机译:提出了一种改进的布谷鸟搜索算法来建立混沌系统的参数。为了提高布谷鸟基本搜索算法的优化能力,将正交设计和模拟退火算法结合到了CS算法中,提高了布谷鸟搜索能力。然后将所提出的算法分别用于建立无噪声和噪声条件下的Lorenz混沌系统和Chen混沌系统的参数。数值结果表明,该算法具有较高的估计精度和可靠性。最后,将结果与CS算法,遗传算法和粒子群优化算法进行了比较,比较结果表明该方法是高效节能的。

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