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Synchronizing Chaotic Systems with Uncertain Model and Unknown Interference Using Sliding Mode Control and Wavelet Neural Networks

机译:使用滑模控制和小波神经网络同步模型和未知干扰的混沌系统

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摘要

A method using sliding mode control (SMC) and wavelet neural networks (WNN) is proposed, investigated and exploited for synchronizing master and slave chaotic systems with uncertain model and unknown interference. In this paper, integral sliding surface and applying WNN for approximating uncertain model and unknown interference are further developed for designing adaptive sliding mode controller. Mexican hat wavelet function is used as activation function in WNN. The adaptive laws of network parameters are derived in the sense of Lyapunov stability analysis so that the tracking errors and convergence of the weights can be guaranteed. The error of synchronization of master-slave chaotic systems can be reached desired level in limited time by using Li function in SMC. Illustrative examples are provided and analyzed to substantiate the efficacy of proposed method for solving the problem of synchronizing master and slave chaotic systems.
机译:提出了一种使用滑模控制(SMC)和小波神经网络(WNN)的方法,研究和利用具有不确定模型和未知干扰的主奴隶混沌系统。在本文中,进一步开发用于设计自适应滑模控制器的整体滑动表面和应用WNN施加WNN。墨西哥帽小波功能用作Wnn的激活功能。网络参数的自适应定律在Lyapunov稳定性分析的意义上得出,以便可以保证权重的跟踪误差和融合。在SMC中使用LI函数,可以在有限的时间内达到所需的级别的主从混沌系统的同步误差。提供和分析说明性实例以证实提出的方法解决了求解主和从混沌系统的问题的方法。

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