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LMI approach to the global robust stability of a larger class of neural networks with delay

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  • Singh, Vimal

Abstract

Sufficient conditions in the form of linear matrix inequality for the uniqueness and global asymptotic stability of the equilibrium point of a large class of uncertain neural networks with delay are presented. The conditions are based on norm-bounded uncertainties. An example is given to show the effectiveness of the obtained results. A comparison is made between the present approach and an earlier approach due to Lu, Rong and Chen. An error is corrected in an earlier publication.

Suggested Citation

  • Singh, Vimal, 2007. "LMI approach to the global robust stability of a larger class of neural networks with delay," Chaos, Solitons & Fractals, Elsevier, vol. 32(5), pages 1927-1934.
    • Handle: RePEc:eee:chsofr:v:32:y:2007:i:5:p:1927-1934
    DOI: 10.1016/j.chaos.2006.01.001
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    1. Cao, Jinde & Ho, Daniel W.C., 2005. "A general framework for global asymptotic stability analysis of delayed neural networks based on LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 24(5), pages 1317-1329.
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    9. Liang, Jinling & Cao, Jinde, 2006. "A based-on LMI stability criterion for delayed recurrent neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 28(1), pages 154-160.
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    12. Singh, Vimal, 2006. "Simplified LMI condition for global asymptotic stability of delayed neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 29(2), pages 470-473.
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    Cited by:

    1. Tian, Junkang & Xu, Dongsheng, 2009. "New asymptotic stability criteria for neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 1916-1922.

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