Input-to-State stability analysis for memristive Cohen-Grossberg-type neural networks with variable time delays

被引：16

作者：

Zhao, Yong ^{[1
,2
,4
]}

Kurths, Juergen ^{[2
,4
,5
]}

Duan, Lixia ^{[3
]}

机构：

[1] Henan Polytech Univ, Sch Math & Informat Sci, Jiaozuo 454000, Peoples R China

[2] Humboldt Univ, Inst Phys, D-12489 Berlin, Germany

[3] North China Univ Technol, Coll Sci, Beijing 100144, Peoples R China

[4] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany

[5] Univ Aberdeen, Inst Complex Syst & Math Biol, Aberdeen AB24 3FX, Scotland

来源：

CHAOS SOLITONS & FRACTALS | 2018年 / 114卷

基金：

中国国家自然科学基金;

关键词：

Input-to-state stability; Memristive neural networks; Nonsmooth analysis; Variable time delays; Lyapunov method; GLOBAL ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; VARYING DELAYS; SYNCHRONIZATION; DISCRETE; CIRCUITS;

D O I：

10.1016/j.chaos.2018.07.021

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, we discussed the input-to-state stability of a class of memristive Cohen-Grossberg-type neural networks with variable time delays. Based on a nonsmooth analysis and set-valued maps, some novel sufficient conditions are obtained for the input-to-state stability of such networks, which include some known results as particular cases. Especially, when the input is zero, it reduced to asymptotical stability of the state. Finally, an illustrative example is presented to illustrate the feasibility and effectiveness of our results. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：364 / 369

页数：6

共 50 条

[21] Further Results on Input-to-State Stability of Stochastic Cohen-Grossberg BAM Neural Networks with Probabilistic Time-Varying Delays
Chandrasekar, A.
Radhika, T.
Zhu, Quanxin
NEURAL PROCESSING LETTERS, 2022, 54 (01) : 613 - 635
[22] Mean-square exponentially input-to-state stability of stochastic Cohen-Grossberg neural networks with time-varying delays
Zhou, Weisong
Teng, Lingying
Xu, Daoyi
NEUROCOMPUTING, 2015, 153 : 54 - 61
[23] Exponential stability of Cohen-Grossberg-type BAM neural networks with time-varying delays via impulsive control
Li, Xiaodi
NEUROCOMPUTING, 2009, 73 (1-3) : 525 - 530
[24] Mean square stability of periodic solution for stochastic Cohen-Grossberg-type BAM neural networks with delays
Miao, Chunfang
Ke, Yunquan
WSEAS Transactions on Mathematics, 2012, 11 (09) : 731 - 741
[25] Exponential stability of periodic solution to Cohen-Grossberg-type BAM networks with time-varying delays
Xiang, Hongjun
Cao, Jinde
NEUROCOMPUTING, 2009, 72 (7-9) : 1702 - 1711
[26] Periodic Solutions of a Cohen-Grossberg-Type BAM Neural Networks with Distributed Delays and Impulses
Liu, Qiming
Xu, Rui
JOURNAL OF APPLIED MATHEMATICS, 2012,
[27] State Bounding of Memristive Cohen-Grossberg Neural Networks with Mixed Delays
Su, Meng
Xue, Yu
Zhang, Xian
2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 4830 - 4835
[28] EXPONENTIAL STABILITY OF PERIODIC SOLUTIONS FOR INERTIAL COHEN-GROSSBERG-TYPE NEURAL NETWORKS
Ke, Yunquan
Miao, Chunfang
NEURAL NETWORK WORLD, 2014, 24 (04) : 377 - 394
[29] Exponential input-to-state stability of impulsive stochastic fuzzy Cohen-Grossberg neural networks with distributed infinite transmission delays
Zhou, Weisong
Shi, Dan
Zhu, Wei
PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC), 2019, : 5715 - 5720
[30] P/q-Type criteria for stability analysis in higher order Cohen-Grossberg-type bidirectional associative memory neural networks with time delays
Xia Y.
Journal of Applied Mathematics and Computing, 2010, 32 (2) : 311 - 328

← 1 2 3 4 5 →