Global exponential convergence for a class of HCNNs with neutral time-proportional delays

Yuehua Yu

Applied Mathematics and Computation, 2016, vol. 285, issue C, 1-7

Abstract: This paper is concerned with a class of high-order cellular neural networks with neutral time-proportional delays. Based on a new differential inequality technique, some sufficient conditions are derived to ensure that all solutions of the addressed system converge exponentially to zero vector, which improve and supplement existing ones. Also, an example and its numerical simulations are given to demonstrate our theoretical results.

Keywords: High-order cellular neural network; Exponential convergence; Neutral time-proportional delay (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:285:y:2016:i:c:p:1-7

DOI: 10.1016/j.amc.2016.03.018

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