Novel global robust exponential stability criterion for uncertain BAM neural networks with time-varying delays

Li Sheng and Huizhong Yang

Chaos, Solitons & Fractals, 2009, vol. 40, issue 5, 2102-2113

Abstract: In this paper, the global robust exponential stability for a class of delayed BAM neural networks with norm-bounded uncertainty is studied. Some less conservative conditions are presented for the global exponential stability of BAM neural networks with time-varying delays by constructing a new class of Lyapunov functionals combined with free-weighting matrices. This novel approach, based on the linear matrix inequality (LMI) technique, removes some existing restrictions on the system’s parameters, and the derived conditions are easy to verify via the LMI toolbox. Comparisons between our results and previous results admit that our results establish a new set of stability criteria for delayed BAM neural networks.

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:40:y:2009:i:5:p:2102-2113

DOI: 10.1016/j.chaos.2007.09.098

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