Delay-Probability-Distribution-Dependent Stability of Uncertain Stochastic Genetic Regulatory Networks with Time-Varying Delays

被引：7
|
作者：
Rakkiyappan, R. [2 ]
Lakshmanan, S. [1 ]
Balasubramaniam, P. [1 ,3 ]
机构：
[1] Deemed Univ, Gandhigram Rural Inst, Dept Math, Gandhigram 624302, Tamil Nadu, India
[2] Bharathiar Univ, Dept Math, Coimbatore 641046, Tamil Nadu, India
[3] Univ Malaya, Inst Math Sci, Fac Sci, Kuala Lumpur 50603, Malaysia
来源：
CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2013年 / 32卷 / 03期
关键词：
Asymptotic stability; Delay-probability-distribution dependent; Stochastic genetic regulatory networks; Linear matrix inequality; Lyapunov-Krasovskii functional; EXPONENTIAL STABILITY; ROBUST STABILITY; NEURAL-NETWORKS; DISCRETE; LOGIC; SWITCHES;
D O I：
10.1007/s00034-013-9595-2
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：
This paper investigates the delay-probability-distribution-dependent stability problem of uncertain stochastic genetic regulatory networks (SGRNs) with time-varying delays. The information of the probability distribution of the time-delay is considered and transformed into parameter matrices of the transferred SGRNs model. Based on the Lyapunov-Krasovskii functional and a stochastic analysis approach, a delay-probability-distribution-dependent sufficient condition is obtained in the linear matrix inequality (LMI) form such that delayed SGRNs are robustly globally asymptotically stable in the mean square for all admissible uncertainties. Three numerical examples are given to illustrate the effectiveness of our theoretical results.
引用
收藏
页码：1147 / 1177
页数：31
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