Delay-dependent state estimation of static neural networks with time-varying and distributed delays
type: Item
instance of: scholarly article
title: Delay-dependent state estimation of static neural networks with time-varying and distributed delays
zbMATH Open document ID: 1407.93375
DOI: 10.1155/2014/951973
author: Lei Shao
author: He Huang
author: Heming Zhao
author: Tingwen Huang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: This paper focuses on studying the state estimation problem of static neural networks with time-varying and distributed delays. By constructing a suitable Lyapunov functional and employing two integral inequalities, a sufficient condition is obtained under which the estimation error system is globally asymptotically stable. It can be seen that this condition is dependent on the two kinds of time delays. To reduce the conservatism of the derived result, Wirtinger inequality is employed to handle a cross term in the time-derivative of Lyapunov functional. It is further shown that the design of the gain matrix of state estimator is transformed to finding a feasible solution of a linear matrix inequality, which is efficiently facilitated by available algorithms. A numerical example is explored to demonstrate the effectiveness of the developed result.
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 7017697
Wikidata QID: Q59071090
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/951973
OpenAlex ID: W2089804235
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description: scientific article; zbMATH DE number 7017697