State estimation for discrete-time stochastic neural networks with mixed delays
type: Item
instance of: scholarly article
title: State estimation for discrete-time stochastic neural networks with mixed delays
zbMATH Open document ID: 1406.92010
DOI: 10.1155/2014/209486
author: Liyuan Hou
author: Hong Zhu
author: Yong Zeng
author: Lin Shi
author: Shouming Zhong
published in: Journal of Applied Mathematics
publication date: 2019-02-01T00:00:00Z
review text: Summary: This paper investigates the analysis problem for stability of discrete-time neural networks (NNs) with discrete- and distribute-time delay. Stability theory and a linear matrix inequality (LMI) approach are developed to establish sufficient conditions for the NNs to be globally asymptotically stable and to design a state estimator for the discrete-time neural networks. Both the discrete delay and distribute delays employ decomposing the delay interval approach, and the Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals, such that a new stability criterion is proposed in terms of linear matrix inequalities (LMIs). Numerical examples are given to demonstrate the effectiveness of the proposed method and the applicability of the proposed method.
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93C55
zbMATH DE Number: 7010572
Wikidata QID: Q59050293
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/209486
OpenAlex ID: W1966731772
DBLP publication ID: journals/jam/HouZZZS14
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description: scientific article; zbMATH DE number 7010572