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

被引：30

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

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