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Neural Processing Letters

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01-12-2016

Exponential stability of a class of competitive neural networks with multi-proportional delays

Authors: Liqun Zhou, Zhongying Zhao

Published in: Neural Processing Letters | Issue 3/2016

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Abstract

In this paper, the exponential stability of a class of competitive neural networks with multi-proportional delays is studied. First, through suitable transformations, a class of competitive neural networks with multi-proportional delays can be equivalently turned into a class of competitive neural networks with multi-constant delays and variable coefficients. By using fixed point theorem, the existence and uniqueness of equilibrium point of the system is proved. Furthermore by constructing appropriate delay differential inequality, two delay-independent and delay-independent sufficient conditions for the exponential stability of equilibrium point are obtained. Finally, several examples and their simulations are given to illustrate the effectiveness of the obtained results.

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Title: Exponential stability of a class of competitive neural networks with multi-proportional delays
Authors: Liqun Zhou
Zhongying Zhao
Publication date: 01-12-2016
Publisher: Springer US
Published in: Neural Processing Letters / Issue 3/2016
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-015-9486-6

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