Improved exponential stability criteria for discrete-time neural networks with time-varying delay

  • Authors:

  • Zixin Liu;Shu Lü;Shouming Zhong;Mao Ye

  • Affiliations:

  • School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China and School of Mathematics and Statistics, Guizhou College of Fin ...;School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China;School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China

  • Venue:
  • Neurocomputing
  • Year:
  • 2010

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Abstract

The problem of robust exponential stability for a class of discrete-time recurrent neural networks with time-varying delay is investigated. By constructing a new augmented Lyapunov-Krasovskii functional, some new delay-dependent stable criteria are obtained. These criteria are formulated in the forms of linear matrix inequality (LMI). Compared with some previous results, the new conditions obtained in this paper are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method.