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New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay

Author

Listed:
  • Zixin Liu
  • Shu Lv
  • Shouming Zhong
  • Mao Ye

Abstract

The robust stability of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By decomposing some connection weight matrices, new Lyapunov-Krasovskii functionals are constructed, and serial new improved stability criteria are derived. These criteria are formulated in the forms of linear matrix inequalities (LMIs). Compared with some previous results, the new results are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method.

Suggested Citation

  • Zixin Liu & Shu Lv & Shouming Zhong & Mao Ye, 2009. "New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay," Discrete Dynamics in Nature and Society, Hindawi, vol. 2009, pages 1-23, July.
    • Handle: RePEc:hin:jnddns:874582
    DOI: 10.1155/2009/874582
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    Cited by:

    1. Balasubramaniam, P. & Lakshmanan, S. & Manivannan, A., 2012. "Robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 45(4), pages 483-495.

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