Anti-synchronization of coupled memristive neutral-type neural networks with mixed time-varying delays via randomly occurring control

• Published in: Nonlinear dynamics Vol. 83; no. 4; pp. 2143 - 2155
• Main Authors: Wang, Weiping, Li, Lixiang, Peng, Haipeng, Wang, Weinan, Kurths, Jürgen, Xiao, Jinghua, Yang, Yixian
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.03.2016; Springer Nature B.V
• Subjects: Automotive Engineering; Classical Mechanics; Control; Controllers; Criteria; Delay; Documents; Dynamical Systems; Engineering; Inclusions; Joining; Linear matrix inequalities; Mathematical analysis; Mathematical models; Matrix methods; Mechanical Engineering; Memristors; Neural networks; Original Paper; Synchronism; Vibration; Randomly occurring control; Mixed delays; Memristive neural networks; Neutral-type
• ISSN: 0924-090X; 1573-269X
• DOI: 10.1007/s11071-015-2471-9

In this paper, a class of coupled memristive neural networks of neutral type with mixed time-varying delays via randomly occurring control is studied in order to achieve anti-synchronization. The model of the coupled memristive neural networks of neutral type with mixed time-varying delays is less conservative than those of traditional memristive neural networks. Some criteria are obtained to guarantee the anti-synchronization between the drive system and the response system. Two kinds of randomly occurring memristor-based controllers are designed. The analysis in this paper employs the differential inclusions theory, linear matrix inequalities, and the Lyapunov functional method. In addition, the new proposed results here are very easy to verify and also extend the results of earlier publications. Numerical examples are given to show the effectiveness of our results.