Extended dissipativity synchronization for Markovian jump recurrent neural networks via memory sampled-data control and its application to circuit theory

被引：8

作者：

Anbuvithya, R. ^{[1
]}

Sri, S. Dheepika ^{[1
]}

Vadivel, R. ^{[2
]}

Hammachukiattikul, P. ^{[2
]}

Park, Choonkil ^{[3
]}

Nallappan, Gunasekaran ^{[4
]}

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] Hanyang Univ, Res Inst Nat Sci, Seoul 04763, South Korea

[4] Toyota Technol Inst, Intelligence Lab, Nagoya, Aichi 4688511, Japan

来源：

INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS | 2022年 / 13卷 / 01期

关键词：

Extended Dissipativity; Markovian Jump Recurrent Neural Networks; Memory sampled - data control; Synchronization; TIME-VARYING DELAYS; STABILITY ANALYSIS; EXPONENTIAL STABILITY;

D O I：

10.22075/ijnaa.2021.25114.2919

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The problem of synchronization with extended dissipativity for Markovian Jump Recurrent Neural Networks (MJRNNs) is investigated. For MJRNNs, a new memory sampled - data extended dissipative control approach is suggested here. Some sufficient conditions in terms of Linear Matrix Inequalities (LMIs) are acquired by suitably establishing a relevant Lyapunov - Krasovskii functional (LKF), wherein the master and the slave system of MJRNNs are quadratically stable. At last, a nu-merical section is provided, along with one of the applications in circuit theory that clearly illustrates the efficacy of the proposed method's performance.

引用

页码：2801 / 2820

页数：20

共 40 条

[1] State estimation for T-S fuzzy Hopfield neural networks via strict output passivation of the error system [J].

Ahn, Choon Ki .

INTERNATIONAL JOURNAL OF GENERAL SYSTEMS, 2013, 42 (05) :503-518

[2] Extended dissipativity and event-triggered synchronization for T-S fuzzy Markovian jumping delayed stochastic neural networks with leakage delays via fault-tolerant control [J].

Ali, M. Syed ;

Vadivel, R. ;

Alsaedi, Ahmed ;

Ahmad, Bashir .

SOFT COMPUTING, 2020, 24 (05) :3675-3694

[3] Design of passivity and passification for delayed neural networks with Markovian jump parameters via non-uniform sampled-data control [J].

Ali, M. Syed ;

Gunasekaran, N. ;

Saravanakumar, R. .

NEURAL COMPUTING & APPLICATIONS, 2018, 30 (02) :595-605

[4] Decentralized Event-Triggered Exponential Stability for Uncertain Delayed Genetic Regulatory Networks with Markov Jump Parameters and Distributed Delays [J].

Ali, M. Syed ;

Vadivel, R. .

NEURAL PROCESSING LETTERS, 2018, 47 (03) :1219-1252

[5] Robust stability of hopfield delayed neural networks via an augmented L-K functional [J].

Ali, M. Syed ;

Gunasekaran, N. ;

Rani, M. Esther .

NEUROCOMPUTING, 2017, 234 :198-204

[6] Finite time decentralized event-triggered communication scheme for neutral-type Markovian jump neural networks with time varying delays [J].

Ali, Syed ;

Vadivel, R. ;

Murugan, Kadarkarai .

CHINESE JOURNAL OF PHYSICS, 2018, 56 (05) :2448-2464

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

Anbuvithya, R. ;

Sri, S. Dheepika ;

Vadivel, R. ;

Gunasekaran, Nallappan ;

Hammachukiattikul, Porpattama .

IEEE ACCESS, 2021, 9 :31454-31466

[8] An improved robust stability result for uncertain neural networks with multiple time delays [J].

Arik, Sabri .

NEURAL NETWORKS, 2014, 54 :1-10

[9] HOW DELAYS AFFECT NEURAL DYNAMICS AND LEARNING [J].

BALDI, P ;

ATIYA, AF .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (04) :612-621

[10] Almost periodic attractor of delayed neural networks with variable coefficients [J].

Cao, JD ;

Chen, AP ;

Huang, X .

PHYSICS LETTERS A, 2005, 340 (1-4) :104-120

← 1 2 3 4 →