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Global exponential stability for uncertain cellular neural networks with multiple time-varying delays via LMI approach

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  • Gau, R.S.
  • Lien, C.H.
  • Hsieh, J.G.

Abstract

The global exponential stability for a class of uncertain delayed cellular neural networks (DCNN) with multiple time-varying delays is considered. Delay-dependent criteria are proposed to guarantee the robust stability of DCNN via linear matrix inequality (LMI) approach. Two classes of uncertainties on feedback matrices are investigated. Some numerical examples are given to illustrate the effectiveness of our results. From the numerical simulation, significant improvement over the recent results can be observed.

Suggested Citation

  • Gau, R.S. & Lien, C.H. & Hsieh, J.G., 2007. "Global exponential stability for uncertain cellular neural networks with multiple time-varying delays via LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 32(4), pages 1258-1267.
    • Handle: RePEc:eee:chsofr:v:32:y:2007:i:4:p:1258-1267
    DOI: 10.1016/j.chaos.2005.11.036
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    References listed on IDEAS

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    8. Zhang, Qiang & Wei, Xiaopeng & Xu, Jin, 2005. "Delay-dependent exponential stability of cellular neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 23(4), pages 1363-1369.
    9. Zhang, Qiang & Wei, Xiaopeng & Xu, Jin, 2005. "New stability conditions for neural networks with constant and variable delays," Chaos, Solitons & Fractals, Elsevier, vol. 26(5), pages 1391-1398.
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    Cited by:

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    4. Yucel, Eylem & Arik, Sabri, 2009. "Novel results for global robust stability of delayed neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 39(4), pages 1604-1614.

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