Global exponential dissipativity of neutral-type BAM inertial neural networks with mixed time-varying delays

被引：32
|
作者：
Duan, Liyan [1 ]
Jian, Jigui [1 ,2 ]
Wang, Baoxian [1 ,2 ]
机构：
[1] China Three Gorges Univ, Coll Sci, Yichang 443002, Hubei, Peoples R China
[2] China Three Gorges Univ, Three Gorges Math Res Ctr, Yichang 443002, Hubei, Peoples R China
来源：
NEUROCOMPUTING | 2020年 / 378卷
基金：
中国国家自然科学基金;
关键词：
BAM inertial neural network; Dissipativity; Globally exponentially attractive set; Neutral-type; Mixed time-varying delay; Inequality; STABILITY ANALYSIS; DISCRETE; SYNCHRONIZATION; STABILIZATION; SYSTEMS; LEAKAGE;
D O I：
10.1016/j.neucom.2019.10.082
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper considers the global exponential dissipativity of neutral-type BAM inertial neural networks with mixed time-varying delays. Firstly, we transform the proposed BAM inertial neural networks to usual one. Secondly, by establishing a new neutral-type differential inequality and employing Lyapunov method and analytical techniques, some novel sufficient conditions in accordance with algebraic and linear matrix inequalities are obtained for the global exponential dissipativity of the addressed neural networks. Moreover, the globally exponentially attractive sets and the exponential convergence rate index are also assessed. Finally, the effectiveness of the obtained results is illustrated by some examples with numerical simulations. (C) 2019 Elsevier B.V. All rights reserved.
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页码：399 / 412
页数：14
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