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Rejecting Chaotic Disturbances Using a Super-Exponential-Zeroing Neurodynamic Approach for Synchronization of Chaotic Sensor Systems

机译:使用超指数归零神经动力学方法抑制混沌干扰实现混沌传感器系统同步

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摘要

Due to the existence of time-varying chaotic disturbances in complex applications, the chaotic synchronization of sensor systems becomes a tough issue in industry electronics fields. To accelerate the synchronization process of chaotic sensor systems, this paper proposes a super-exponential-zeroing neurodynamic (SEZN) approach and its associated controller. Unlike the conventional zeroing neurodynamic (CZN) approach with exponential convergence property, the controller designed by the proposed SEZN approach inherently possesses the advantage of super-exponential convergence property, which makes the synchronization process faster and more accurate. Theoretical analyses on the stability and convergence advantages in terms of both faster convergence speed and lower error bound within the task duration are rigorously presented. Moreover, three synchronization examples substantiate the validity of the SEZN approach and the related controller for synchronization of chaotic sensor systems. Comparisons with other approaches such as the CZN approach, show the convergence superiority of the proposed SEZN approach. Finally, extensive tests further investigate the impact on convergence performance by choosing different values of design parameter and initial state.
机译:由于在复杂应用中存在随时间变化的混沌干扰,因此传感器系统的混沌同步成为工业电子领域的难题。为了加快混沌传感器系统的同步过程,本文提出了一种超指数归零神经动力学(SEZN)方法及其相关控制器。与具有指数收敛特性的传统调零神经动力学(CZN)方法不同,所提出的SEZN方法设计的控制器固有地具有超指数收敛特性的优点,这使得同步过程更快,更准确。严格地给出了在任务持续时间内在更快的收敛速度和更低的误差范围方面的稳定性和收敛优势的理论分析。此外,三个同步示例证实了SEZN方法和相关控制器对混沌传感器系统同步的有效性。与其他方法(例如CZN方法)的比较表明,提出的SEZN方法具有收敛优势。最后,广泛的测试通过选择不同的设计参数和初始状态值进一步研究了对收敛性能的影响。

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