Global dissipativity of high-order Hopfield bidirectional associative memory neural networks with mixed delays

被引：25
作者：
Aouiti, Chaouki [1 ]
Sakthivel, Rathinasamy [2 ,3 ]
Touati, Farid [1 ]
机构：
[1] Univ Carthage, Fac Sci Bizerta, Dept Math, Res Units Math & Applicat UR13ES47, Zarzouna 7021, Bizerta, Tunisia
[2] Bharathiar Univ, Dept Appl Math, Coimbatore 641046, Tamil Nadu, India
[3] Sungkyunkwan Univ, Dept Math, Suwon 440746, South Korea
来源：
NEURAL COMPUTING & APPLICATIONS | 2020年 / 32卷 / 14期
关键词：
BAM high-order neural networks; Global dissipativity; Global exponential dissipativity; Time-varying delay; Distributed delays; TIME-VARYING DELAY; ALMOST-PERIODIC SOLUTION; EXPONENTIAL STABILITY; STATE ESTIMATION; DYNAMICAL-SYSTEMS; LEAKAGE; EXISTENCE; DISCRETE; DESIGN;
D O I：
10.1007/s00521-019-04552-8
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, the problem of the global dissipativity of high-order Hopfield bidirectional associative memory neural networks with time-varying coefficients and distributed delays is discussed. By using Lyapunov-Krasovskii functional method, inequality techniques and linear matrix inequalities, a novel set of sufficient conditions for global dissipativity and global exponential dissipativity for the addressed system is developed. Further, the estimations of the positive invariant set, globally attractive set and globally exponentially attractive set are found. Finally, two examples with numerical simulations are provided to support the feasibility of the theoretical findings.
引用
收藏
页码：10183 / 10197
页数：15
相关论文
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