Further stability criterion on delayed recurrent neural networks based on reciprocal convex technique
type: Item
instance of: scholarly article
title: Further stability criterion on delayed recurrent neural networks based on reciprocal convex technique
zbMATH Open document ID: 1264.93193
DOI: 10.1155/2012/829037
author: Guobao Zhang
author: Tao Li
author: Shumin Fei
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: Together with Lyapunov-Krasovskii functional theory and reciprocal convex technique, a new sufficient condition is derived to guarantee the global stability for recurrent neural networks with both time-varying and continuously distributed delays, in which one improved delay-partitioning technique is employed. The LMI-based criterion heavily depends on both the upper and lower bounds on state delay and its derivative, which is different from the existent ones and has more application areas as the lower bound of delay derivative is available. Finally, some numerical examples can illustrate the reduced conservatism of the derived results by thinning the delay interval.
Mathematics Subject Classification ID: 93D05
Mathematics Subject Classification ID: 34K20
zbMATH DE Number: 6173613
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/829037
OpenAlex ID: W2070085040
Wikidata QID: Q58912111
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description: scientific article; zbMATH DE number 6173613