A note on exponential convergence of neural networks with unbounded distributed delays

Tianguang Chu and Haifeng Yang

Chaos, Solitons & Fractals, 2007, vol. 34, issue 5, 1538-1545

Abstract: This note examines issues concerning global exponential convergence of neural networks with unbounded distributed delays. Sufficient conditions are derived by exploiting exponentially fading memory property of delay kernel functions. The method is based on comparison principle of delay differential equations and does not need the construction of any Lyapunov functionals. It is simple yet effective in deriving less conservative exponential convergence conditions and more detailed componentwise decay estimates. The results of this note and [Chu T. An exponential convergence estimate for analog neural networks with delay. Phys Lett A 2001;283:113–8] suggest a class of neural networks whose globally exponentially convergent dynamics is completely insensitive to a wide range of time delays from arbitrary bounded discrete type to certain unbounded distributed type. This is of practical interest in designing fast and reliable neural circuits. Finally, an open question is raised on the nature of delay kernels for attaining exponential convergence in an unbounded distributed delayed neural network.

Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:34:y:2007:i:5:p:1538-1545

DOI: 10.1016/j.chaos.2006.04.033

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