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

Author

Wang, Zhongsheng ; Xiang, Dan ; Yan, Nin

Author_Institution

Coll. of Autom., Guangdong Polytech. Normal Univ., Guangzhou

Volume

2

fYear

2008

fDate

18-20 Oct. 2008

Firstpage

409

Lastpage

413

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.

Keywords

Hopfield neural nets; adaptive control; cellular neural nets; decentralised control; delays; feedback; synchronisation; time-varying systems; Hopfield neural networks; Razumikhin-type theorem; adaptive control; cellular neural networks; chaotic neural networks; decentralized linear-feedback control; globally exponential synchronization; parameter regulation; time-varying delays; time-varying neural networks; Adaptive control; Cellular neural networks; Chaos; Chaotic communication; Computer networks; Delay effects; Delay lines; Hopfield neural networks; Neural networks; Neurons; Adaptive control; Chaotic Neural Networks; Globally Exponential Synchronization; Parameter Regulation; Stability Theorem; Time-Varying Delays;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2008. ICNC '08. Fourth International Conference on

Conference_Location

Jinan

Print_ISBN

978-0-7695-3304-9

Type

conf

DOI

10.1109/ICNC.2008.32

Filename

4667027