New Stabilization Method for Delayed Discrete-Time Cohen-Grossberg BAM Neural Networks

被引：7
作者：
Cong, Er-Yong [1 ,3 ]
Han, Xiao [3 ]
Zhang, Xian [2 ]
机构：
[1] Harbin Univ, Dept Math, Harbin 150086, Peoples R China
[2] Heilongjiang Univ, Sch Math Sci, Harbin 150080, Peoples R China
[3] Jilin Univ, Sch Math, Changchun 130012, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷
关键词：
Control theory; Stability; Delays; State feedback; Biological neural networks; Closed loop systems; Discrete-time Cohen-Grossberg BAM neural network; stabilization; global exponential stability; GLOBAL EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY;
D O I：
10.1109/ACCESS.2020.2997905
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper deals with the state feedback stabilization problem of delayed discrete-time Cohen-Grossberg BAM neural networks. By the mathematical induction method, stabilizable conditions are derived to ensure that the resulting closed-loop system is globally exponentially stable, and thereby, the desired state feedback controller is designed. These stabilizable conditions are very simple, which can easily verified by using the standard toolbox software (for example, MATLAB). The proposed approach is directly based on the definition of global exponential stability, and does not involve the construction of any Lyapunov-Krasovskii functional. For a special case, it is theoretical proven that the proposed method is superior to an existing one. Moreover, several illustrative examples are given to validate the success of the derived theoretical results.
引用
收藏
页码：99327 / 99336
页数：10
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