Exponential stability of neural networks with variable delays via LMI approach

Haifeng Yang, Tianguang Chu and Cishen Zhang

Chaos, Solitons & Fractals, 2006, vol. 30, issue 1, 133-139

Abstract: This paper presents sufficient conditions for global asymptotic/exponential stability of neural networks with time-varying delays. By using appropriate Lyapunov–Krasovskii functionals, we derive stability conditions in terms of linear matrix inequalities (LMIs). This is convenient for numerically checking the system stability using the powerful MATLAB LMI Toolbox. Compared with some earlier work, our result does not require any restriction on the derivative of the delay function. Numerical example shows the efficiency and less conservatism of the present result.

Date: 2006
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:30:y:2006:i:1:p:133-139

DOI: 10.1016/j.chaos.2005.08.134

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