Passivity and passification of memristor-based complex-valued recurrent neural networks with interval time-varying delays

被引:47
作者
Rakkiyappan, R. [1 ]
Sivaranjani, K. [1 ]
Velmurugan, G. [1 ]
机构
[1] Bharathiar Univ, Dept Math, Coimbatore 641046, Tamil Nadu, India
关键词
Passivity; Passification; Complex-valued neural networks; Memristor; Linear matrix inequality (LMI); EXPONENTIAL PASSIVITY; SYNCHRONIZATION; STABILITY; ELEMENT;
D O I
10.1016/j.neucom.2014.04.034
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we made an effort to investigate the passivity and passification of memristor-based complex-valued recurrent neural networks (MCVNNs) with interval time-varying delays. By constructing proper Lyapunov-Krasovskii functional and using the characteristic function method, passivity conditions are derived in terms of linear matrix inequalities (LMIs). Then, based on the derived passivity condition, the desired feedback controller is designed, which ensures the MCVNNs with interval time-varying delays to be passive. Finally, numerical examples are given to illustrate the effectiveness of the proposed theoretical results. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:391 / 407
页数:17
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