Analysis of Markovian Jump Stochastic Cohen-Grossberg BAM Neural Networks with Time Delays for Exponential Input-to-State Stability

被引：110

作者：

Radhika, T. ^{[1
]}

Chandrasekar, A. ^{[1
]}

Vijayakumar, V. ^{[1
]}

Zhu, Quanxin ^{[2
]}

机构：

[1] Vellore Inst Technol, Sch Adv Sci, Dept Math, Vellore 632014, Tamil Nadu, India

[2] Hunan Normal Univ, Sch Math & Stat, MOE LCSM, Changsha 410081, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2023年 / 55卷 / 08期

关键词：

BAM neural networks; Markovian jump; Time-varying delay; Input-to-state stable; Stochastic systems;

D O I：

10.1007/s11063-023-11364-4

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 article, the Input-to-state stability theory is used to investigate the stochastic Cohen-Grossberg bidirectional associative memory neural network with time-varying delay. In addition, Markovian jump parameters are considered in this model to determine the continuous-time, discrete-state Markov chain. By utilizing Lyapunov functional and weak infinitesimal generator the algebraic conditions are derived for Input-to-state criteria. In the end, a numerical example is given to show the merits of the given method.

引用

页码：11055 / 11072

页数：18

共 21 条

[1] Global exponential stability and periodicity of recurrent neural networks with time delays [J].

Cao, JD ;

Wang, J .

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2005, 52 (05) :920-931

[2] Input-to-State Stability for Stochastic Delay Neural Networks with Markovian Switching [J].

Fan, Yumei ;

Chen, Huabin .

NEURAL PROCESSING LETTERS, 2021, 53 (06) :4389-4406

[3] Input-to-state Stabilization of Delayed Semi-Markovian Jump Neural Networks Via Sampled-Data Control [J].

He, Ling ;

Wu, Wenhuang ;

Yao, Guangshun ;

Zhou, Jianping .

NEURAL PROCESSING LETTERS, 2023, 55 (03) :3245-3266

[4] Exponential stability analysis of stochastic delayed cellular neural network [J].

Hu, J ;

Zhong, SM ;

Liang, L .

CHAOS SOLITONS & FRACTALS, 2006, 27 (04) :1006-1010

[5] New results on exponential input-to-state stability analysis of memristor based complex-valued inertial neural networks with proportional and distributed delays [J].

Iswarya, M. ;

Raja, R. ;

Cao, C. ;

Niezabitowski, M. ;

Alzabut, J. ;

Maharajan, C. .

MATHEMATICS AND COMPUTERS IN SIMULATION, 2022, 201 :440-461

[6] BIDIRECTIONAL ASSOCIATIVE MEMORIES [J].

KOSKO, B .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1988, 18 (01) :49-60

[7] Stability in impulsive Cohen-Grossberg-type BAM neural networks with distributed delays [J].

Li, Kelin ;

Zhang, Liping ;

Zhang, Xinhua ;

Li, Zuoan .

APPLIED MATHEMATICS AND COMPUTATION, 2010, 215 (11) :3970-3984

[8] Global asymptotic stability of stochastic Cohen-Grossberg-type BAM neural networks with mixed delays: An LMI approach [J].

Li, Xiaodi ;

Fu, Xilin .

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2011, 235 (12) :3385-3394

[9] Equilibria and Stability Analysis of Cohen-Grossberg BAM Neural Networks on Time Scale [J].

Liu Mingshuo ;

Fang Yong ;

Dong Huanhe .

JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY, 2022, 35 (04) :1348-1373

[10] Finite-time stabilization for positive Markovian jumping neural networks [J].

Ren, Chengcheng ;

He, Shuping .

APPLIED MATHEMATICS AND COMPUTATION, 2020, 365

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