Robustly Exponential Stability Analysis for Discrete-Time Stochastic Neural Networks with Interval Time-Varying Delays
作者: Yali DongShuang LiangLiangliang Guo
作者单位: 1Tianjin Polytechnic University
刊名: Neural Processing Letters, 2017, Vol.46 (1), pp.135-158
来源数据库: Springer Nature Journal
DOI: 10.1007/s11063-016-9575-1
关键词: Stochastic neural networksDiscrete-timeRobust exponential stabilityRobust exponential stabilizationTime-varying delayLinear matrix inequalities
原始语种摘要: This paper deals with the problems of the global exponential stability and stabilization for a class of uncertain discrete-time stochastic neural networks with interval time-varying delay. By using the linear matrix inequality method and the free-weighting matrix technique, we construct a new Lyapunov–Krasovskii functional and establish new sufficient conditions to guarantee that the uncertain discrete-time stochastic neural networks with interval time-varying delay are globally exponential stable in the mean square. Furthermore, we extend our consideration to the stabilization problem for a class of discrete-time stochastic neural networks. Based on the state feedback control law, some novel delay-dependent criteria of the robust exponential stabilization for a class of discrete-time...
全文获取路径: Springer Nature  (合作)
影响因子:1.24 (2012)

  • exponential 指数的
  • varying 变化的
  • neural 神经系统的
  • delay 延时
  • globally 世界上
  • uncertain 不确实的
  • robust 牢固的
  • stabilization 稳定
  • class 
  • effectiveness 有效性