Year: 2016
Author: Liangbo Chen, Zhenkun Huang
Annals of Applied Mathematics, Vol. 32 (2016), Iss. 3 : pp. 234–248
Abstract
This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2016-AAM-20640
Annals of Applied Mathematics, Vol. 32 (2016), Iss. 3 : pp. 234–248
Published online: 2016-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 15
Keywords: global asymptotic stability global exponential stability neural networks on time scales.