Exponential Stabilization of Stochastic Memristive Recurrent Neural Networks Under Periodically Intermittent State Feedback Control

被引：7
作者：
Li, Xiaofan [1 ,2 ]
Fang, Jian-an [2 ]
Li, Huiyuan [2 ]
机构：
[1] Yancheng Inst Technol, Sch Elect Engn, Yancheng 224051, Jiangsu, Peoples R China
[2] Donghua Univ, Sch Informat Sci & Technol, Shanghai 201620, Peoples R China
来源：
ASIAN JOURNAL OF CONTROL | 2020年 / 22卷 / 02期
关键词：
Memristive neural networks; exponential stabilization; stochastic perturbations; intermittent control; SYNCHRONIZATION; STABILITY;
D O I：
10.1002/asjc.1952
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the exponential stabilization problem is investigated for a class of memristive time-varying delayed neural networks with stochastic disturbance via periodically intermittent state feedback control. First, a periodically intermittent state feedback control rule is designed for the exponential stabilization of stochastic memristive time-varying delayed neural networks. Then, by adopting appropriate Lyapunov-Krasovskii functionals in light of the Lyapunov stability theory, some novel stabilization criteria are obtained to guarantee exponential stabilization of stochastic memristive time-varying delayed neural networks via periodically intermittent state feedback control. Compared with existing results on stabilization of stochastic memristive time-varying delayed neural networks, the obtained stabilization criteria in this paper are not difficult to verify. Finally, an illustrative example is given to illustrate the validity of the obtained results.
引用
收藏
页码：897 / 907
页数：11
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