Letters: Exponential synchronization of chaotic neural networks with mixed delays

Authors:
Tao Li;Shu-min Fei;Qing Zhu;Shen Cong
Affiliations:
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, Jiangsu, China;Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, Jiangsu, China;Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, Jiangsu, China;School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
Venue:
Neurocomputing
Year:
2008

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Abstract

This paper deals with the synchronization problem of the chaotic neural networks with time-varying and distributed time-varying delays. Based on the drive-response concept, LMI approach and the Lyapunov stability theorem, a novel control method is presented and two sufficient conditions have been obtained to ensure the global exponential stability for the error system which helps the drive system synchronize with the response system. In addition, the activation functions are assumed to be of more general descriptions, which generalizes and improves those earlier methods. Finally, two numerical examples are given to demonstrate the effectiveness of presented synchronization scheme.