Extended Dissipativity and Non-Fragile Synchronization for Recurrent Neural Networks With Multiple Time-Varying Delays via Sampled-Data Control

被引：33

作者：

Anbuvithya, R. ^{[1
]}

Sri, S. Dheepika ^{[1
]}

Vadivel, R. ^{[2
]}

Gunasekaran, Nallappan ^{[3
]}

Hammachukiattikul, Porpattama ^{[2
]}

机构：

[1] Sri Sarada Coll Women Autonomous, Dept Math, Salem 636016, India

[2] Phuket Rajabhat Univ, Fac Sci & Technol, Dept Math, Phuket 83000, Thailand

[3] Toyota Technol Inst, Dept Adv Sci & Technol, Computat Intelligence Lab, Nagoya, Aichi 4688511, Japan

来源：

IEEE ACCESS | 2021年 / 9卷

关键词：

Delays; Synchronization; Linear matrix inequalities; Symmetric matrices; Recurrent neural networks; Neurons; Stability criteria; Dissipative analysis; multiple time-varying delay; recurrent neural networks; synchronization; sampled-data control;

D O I：

10.1109/ACCESS.2021.3060044

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 extended dissipativity and non-fragile synchronization of delayed recurrent neural networks (RNNs) with multiple time-varying delays and sampled-data control. A suitable Lyapunov-Krasovskii Functional (LKF) is built up to prove the quadratically stable and extended dissipativity condition of delayed RNNs using Jensen inequality and limited Bessel-Legendre inequality approaches. A non-fragile sampled-data approach is applied to investigate the problem of neural networks with multiple time-varying delays, which ensures that the master system synchronizes with the slave system and is designed with respect to the solutions of Linear Matrix Inequalities (LMIs). The effectiveness of the suggested approach is established by providing suitable simulations using MATLAB LMI control toolbox. Finally, numerical examples and comparative results are provided to illustrate the adequacy of the planned control scheme.

引用

页码：31454 / 31466

页数：13

共 31 条

[1] Stability analysis of delayed neural networks [J].

Arik, S .

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, 2000, 47 (07) :1089-1092

[2]

Brogliato B., 2007, THEORY APPL, V2

[3] Global asymptotical stability of recurrent neural networks with multiple discrete delays and distributed delays [J].

Cao, Jinde ;

Yuan, Kun ;

Li, Han-Xiong .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2006, 17 (06) :1646-1651

[4] Exponential Synchronization for Delayed Dynamical Networks via Intermittent Control: Dealing With Actuator Saturations [J].

Chen, Yonggang ;

Wang, Zidong ;

Shen, Bo ;

Dong, Hongli .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (04) :1000-1012

[5] Projective synchronization of fuzzy memristive neural networks with pinning impulsive control [J].

Fu, Qianhua ;

Zhong, Shouming ;

Jiang, Wenbo ;

Xie, Wenqian .

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2020, 357 (15) :10387-10409

[6] Robust Stabilization of Memristor-based Coupled Neural Networks with Time-varying Delays [J].

Fu, Qianhua ;

Cai, Jingye ;

Zhong, Shouming .

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2019, 17 (10) :2666-2676

[7] Dissipativity and passivity analysis for memristor-based neural networks with leakage and two additive time-varying delays [J].

Fu, Qianhua ;

Cai, Jingye ;

Zhong, Shouming ;

Yu, Yongbin .

NEUROCOMPUTING, 2018, 275 :747-757

[8]

Gu K., 2003, CONTROL ENGN SER BIR, DOI 10.1007/978-1-4612-0039-0

[9] An integral inequality in the stability problem of time-delay systems [J].

Gu, KQ .

PROCEEDINGS OF THE 39TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5, 2000, :2805-2810

[10] Sampled-data synchronization of delayed multi-agent networks and its application to coupled circuit [J].

Gunasekaran, Nallappan ;

Zhai, Guisheng ;

Yu, Qiang .

NEUROCOMPUTING, 2020, 413 :499-511

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