An LMI approach for exponential synchronization of switched stochastic competitive neural networks with mixed delays

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• 作者：Xinsong Yang (1) xinsongyang@163.com
  Chuangxia Huang (2) cxiahuang@126.com
  Jinde Cao (3) jdcao@seu.edu.cn
• 关键词：Switched systems – ; Competitive neural networks – ; Exponential synchronization – ; Unbounded distributed delay – ; Vector ; form noise – ; LMI
• 刊名：Neural Computing & Applications
• 出版年：2012
• 出版时间：November 2012
• 年：2012
• 卷：21
• 期：8
• 页码：2033-2047
• 全文大小：1.0 MB
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• 作者单位：1. Department of Mathematics, Honghe University, Mengzi, 661100 Yunnan, China2. Department of Mathematics, College of Mathematics and Computing Science, Changsha University of Science and Technology, Changsha, 410114 Hunan, China3. Department of Mathematics, Southeast University, Nanjing, 210096 China
• ISSN：1433-3058

文摘

This paper investigates the problem of exponential synchronization of switched stochastic competitive neural networks (SSCNNs) with both interval time-varying delays and distributed delays. The distributed delays can be unbounded or bounded; the stochastic perturbation is of the form of multi-dimensional Brownian motion, and the networks are governed by switching signals with average dwell time. Based on new multiple Lyapunov-Krasovkii functionals, the free-weighting matrix method, Newton-Leibniz formulation, as well as the invariance principle of stochastic differential equations, two sufficient conditions ensuring the exponential synchronization of drive-response SSCNNs are developed. The provided conditions are expressed in terms of linear matrix inequalities, which are dependent on not only both lower and upper bounds of the interval time-varying delays but also delay kernel of unbounded distributed delays or upper bounds for bounded distributed delays. Control gains and average dwell time restricted by given conditions are designed such that they are applicable in practice. Numerical simulations are given to show the effectiveness of the theoretical results.