Improved Synchronization Analysis via Looped-Lyapunov for Stochastic Markovian Jump Neural Networks

被引：3

作者：

Ganesan, Bhuvaneshwari ^{[1
]}

Annamalai, Manivannan ^{[1
]}

机构：

[1] Vellore Inst Technol, Sch Adv Sci, Div Math, Chennai 600127, India

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2023年 / 70卷 / 09期

关键词：

Synchronization; stochastic neural networks with Markovain jump; sampled-data control; time delay; TIME-VARYING DELAYS; STABILITY ANALYSIS; DEPENDENT STABILITY; CRITERIA;

D O I：

10.1109/TCSII.2023.3262819

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The synchronization problem of delay-dependent stochastic Markovian jump neural networks (SMJNNs) is examined in this short note. A sampled data controller is designed for this synchronization problem, which synchronizes the uncontrolled and controlled SMJNNs. A looped Lyapunov functional is presented that contains the sampling information, and it is not required to be zero at t(k) and is not required to be continuous. Instead, it must satisfy the condition that V(t(k)(-)) >= 0 and V(t(k)) = 0 or V(t(k)(-)) = 0 and V(t(k)) <= 0. To ensure stochastic stability in the mean square of the error system, sufficient conditions are obtained by using the Ito's formula, integral inequalities, which are given as linear matrix inequalities (LMIs). The proposed results are validated by comparing them with existing results in numerical examples.

引用

页码：3388 / 3392

页数：5

共 23 条

[1] Sampled-Data Synchronization of Stochastic Markovian Jump Neural Networks With Time-Varying Delay [J].

Chen, Guoliang ;

Xia, Jianwei ;

Park, Ju H. ;

Shen, Hao ;

Zhuang, Guangming .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (08) :3829-3841

[2] Sampled-data stabilization analysis of neural-network-based control systems: A discontinuous bilateral looped-functional approach [J].

Dong, Shiyu ;

Zhu, Hong ;

Zhong, Shouming ;

Shi, Kaibo ;

Zhang, Zhenzhen .

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2022, 111

[3] Global exponential synchronization of discrete-time high-order switched neural networks and its application to multi-channel audio encryption [J].

Dong, Zeyu ;

Wang, Xin ;

Zhang, Xian ;

Hu, Mengjie ;

Dinh, Thach Ngoc .

NONLINEAR ANALYSIS-HYBRID SYSTEMS, 2023, 47

[4] Synchronization of Stochastic Neural Networks Using Looped-Lyapunov Functional and Its Application to Secure Communication [J].

Ganesan, Bhuvaneshwari ;

Mani, Prakash ;

Shanmugam, Lakshmanan ;

Annamalai, Manivannan .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 35 (04) :5198-5210

[5]

Gu K., 2003, CONTROL ENGN SER BIR

[6] New delay-variation-dependent stability for neural networks with time-varying delay [J].

Li, Tao ;

Yang, Xin ;

Yang, Pu ;

Fei, Shumin .

NEUROCOMPUTING, 2013, 101 :361-369

[7] Triple-integral method for the stability analysis of delayed neural networks [J].

Liu, Zixin ;

Yu, Jian ;

Xu, Daoyun ;

Peng, Dingtao .

NEUROCOMPUTING, 2013, 99 :283-289

[8] Impulsive synchronization of coupled delayed neural networks with actuator saturation and its application to image encryption [J].

Ouyang, Deqiang ;

Shao, Jie ;

Jiang, Haijun ;

Nguang, Sing Kiong ;

Shen, Heng Tao .

NEURAL NETWORKS, 2020, 128 :158-171

[9] Passivity and stability analysis of neural networks with time-varying delays via extended free-weighting matrices integral inequality [J].

Park, M. J. ;

Kwon, O. M. ;

Ryu, J. H. .

NEURAL NETWORKS, 2018, 106 :67-78

[10] Stability analysis of sampled-data systems via novel Lyapunov functional method [J].

Sheng, Zhaoliang ;

Lin, Chong ;

Chen, Bing ;

Wang, Qing-Guo .

INFORMATION SCIENCES, 2022, 585 :559-570

← 1 2 3 →