Decentralized Event-Triggered Exponential Stability for Uncertain Delayed Genetic Regulatory Networks with Markov Jump Parameters and Distributed Delays

被引：22

作者：

Ali, M. Syed ^{[1
]}

Vadivel, R. ^{[1
]}

机构：

[1] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India

来源：

NEURAL PROCESSING LETTERS | 2018年 / 47卷 / 03期

关键词：

Event-triggered; Genetic regulatory networks (GRNs); Markovian jump parameters; Lyapunov-Krasovskii functional; Linear matrix inequality; NEURAL-NETWORKS; ROBUST STABILITY; STOCHASTIC STABILITY; NEUTRAL-TYPE; DISCRETE; SYSTEMS; SYNCHRONIZATION; MODEL;

D O I：

10.1007/s11063-017-9695-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper is concerned with the stability problem for a class of decentralized event-triggered exponential stability for uncertain delayed genetic regulatory networks (GRNs) with Markov jump parameters and distributed delays. In order to reduce the information communication burden, the decentralized event-triggered mechanism is proposed in this paper. Exponential stability for the proposed GRNs are studied by the Lyapunov method and the matrix inequality techniques. Some new sufficient conditions are obtained to ensure the global exponential stability of the proposed GRNs. Furthermore, the proposed LMI results are computationally efficient which are easy to be verified via the Matlab LMI toolbox. In addition, four numerical examples are provided to illustrate the effectiveness of the theoretical results.

引用

页码：1219 / 1252

页数：34

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