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

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.