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Parameter Identification of Chaotic Systems Using an Improved Artificial Bee Colony Algorithm

机译:改进的人工蜂群算法在混沌系统参数辨识中的应用

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Parameter identification is very important for control and synchronization of chaotic system. In this paper, the problem of parameter identification of chaotic system is studied from the viewpoint of optimization. The parameter identification problem is converted to a multi-dimension parameter optimization problem. An improved artificial bee colony algorithm based on exchange neighborhood structure(EN-ABC) is proposed to solve the optimization problem. The proposed EN-ABC algorithm is applied to identify parameters of Lorenz chaotic system and time delay Logistic chaotic system. Simulation results show that EN-ABC algorithm is more promising and effective than ABC and GA algorithms for the parameter identification of chaotic systems.
机译:参数识别对于混沌系统的控制和同步非常重要。本文从优化的角度研究了混沌系统的参数辨识问题。将参数识别问题转换为多维参数优化问题。提出了一种基于交换邻域结构的改进人工蜂群算法(EN-ABC)来解决优化问题。提出的EN-ABC算法被用于识别Lorenz混沌系统和时滞Logistic混沌系统的参数。仿真结果表明,对于混沌系统的参数辨识,EN-ABC算法比ABC算法和GA算法更具前景和有效性。

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