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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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    Cited by:

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    3. Fei Luo & Weiyi Hu & Enli Wu & Xiufang Yuan, 2024. "Global Exponential Stability of Impulsive Delayed Neural Networks with Parameter Uncertainties and Reaction–Diffusion Terms," Mathematics, MDPI, vol. 12(15), pages 1-15, July.
    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.
    5. Jinxiu Pi & Hui Yang & Yadong Shu & Chongyi Zhong & Guanghui Yang, 2020. "The Stability of Two-Community Replicator Dynamics with Discrete Multi-Delays," Mathematics, MDPI, vol. 8(12), pages 1-17, November.

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