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Global robust exponential stability of delayed neural networks: An LMI approach

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  • Ou, Ou

Abstract

In this paper, the problems of determining the robust exponential stability and estimating the exponential convergence rate for neural networks with parametric uncertainties and time delay are studied. Based on Lyapunov–Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique, some delay-dependent criteria are derived to guarantee global robust exponential stability. The exponential convergence rate can be easily estimated via these criteria.

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  • Ou, Ou, 2007. "Global robust exponential stability of delayed neural networks: An LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 32(5), pages 1742-1748.
    • Handle: RePEc:eee:chsofr:v:32:y:2007:i:5:p:1742-1748
    DOI: 10.1016/j.chaos.2005.12.026
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    References listed on IDEAS

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    1. Li, Chuandong & Liao, Xiaofeng & Zhang, Rong & Prasad, Ashutosh, 2005. "Global robust exponential stability analysis for interval neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 25(3), pages 751-757.
    2. Zhang, Hongbin & Li, Chunguang & Liao, Xiaofeng, 2005. "A note on the robust stability of neural networks with time delay," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 357-360.
    3. Arik, Sabri, 2005. "Global robust stability analysis of neural networks with discrete time delays," Chaos, Solitons & Fractals, Elsevier, vol. 26(5), pages 1407-1414.
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