Globally Exponential Synchronization and Parameter Regulation of Chaotic Neural Networks with Time-Varying Delays via Adaptive Control

Zhongsheng Wang, Dan Xiang, Nin Yan
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引用次数: 1

Abstract

The paper aims to present a globally exponential synchronization and parameter regulation scheme for a class of time-varying neural networks, which covers the Hopfield neural networks and cellular neural networks. By combining the adaptive control method and the Razumikhin-type theorem, a delay-independent and decentralized linear-feedback control with appropriate updated law is designed to achieve the globally exponential synchronization. The regulating law of parameters can be directly constructed. Hopfield neural networks with time-varying delays is given to show the effectiveness of the presented synchronization scheme.
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基于自适应控制的时变时滞混沌神经网络全局指数同步与参数调节
本文提出了一类时变神经网络的全局指数同步和参数调节方案,该方案涵盖了Hopfield神经网络和细胞神经网络。将自适应控制方法与razumikhin型定理相结合,设计了一种具有适当更新律的时滞无关的分散线性反馈控制,以实现全局指数同步。可以直接构造参数的调节律。以具有时变延迟的Hopfield神经网络为例,验证了所提同步方案的有效性。
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