基于RBF神经网络的超跳混沌系统自适应滑模同步

2021 11th International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2021-12-03 DOI:10.1109/ICICIP53388.2021.9642190

Baojie Zhang

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引用次数: 0

摘要

研究了具有不同结构的超跳混沌系统的同步问题。除了阶数外，系统在外界干扰下是未知的。我们采用滑模控制方法来处理外部干扰。提出径向基函数(RBF)神经网络来逼近未知系统。基于RBF神经网络，介绍了不同类型超跳混沌系统的自适应滑模同步。数值结果表明了该同步方案的有效性。

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Adaptive Sliding Mode Synchronization of Different Hyperjerk Chaotic Systems Using RBF Neural Network

In this paper, we consider the synchronization of hyperjerk chaotic systems with different structures. Besides the order, the systems are unknown with external disturbances. We use sliding mode control method to deal with the external disturbances. Radial basis function (RBF) neural network is proposed to approximate the unknown system. Based on RBF neural network, adaptive sliding mode synchronization of different hyperjerk chaotic systems are introduced. Numerical results show the effectiveness of the synchronization scheme.

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2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)

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