Journal of Guangdong University of Technology ›› 2017, Vol. 34 ›› Issue (04): 84-88.doi: 10.12052/gdutxb.160010

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Input-to-state Stability for Stochastic Neural Networks with Time-Varying Delay

Zhang Zhi-zhong, Peng Shi-guo   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2016-01-16 Online:2017-07-09 Published:2017-07-09

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Abstract:

A new stability criterion based on the linear matrix inequality (LMI) input-to-state stability criterion is given. Based on model transformation, input-to-state stability of Stochastic neural networks with Time-Varying Delay is given a sufficient condition in form of linear matrix inequality by constructing appropriate Lyapunov-Krasovskii functional, stochastic analysis theory and Itô's formula and applying some inequality approach. Then, the proposed method is proven to be less conservative by illustrating with the numerical examples, which shows the effectiveness of the method.

Key words: stochastic neural network, time-varying delay, Input-to-state stability, Lyapunov-Krasovskii functional, linear matrix inequality

CLC Number: 

  • TP273

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