Stability analysis of stochastic Markovian jump neural networks with different time scales and randomly occurred nonlinearities based on delay-partitioning projection approach
type: Item
instance of: scholarly article
title: Stability analysis of stochastic Markovian jump neural networks with different time scales and randomly occurred nonlinearities based on delay-partitioning projection approach
zbMATH Open document ID: 1291.60117
DOI: 10.1155/2013/212469
author: Jianmin Duan
author: Manfeng Hu
author: Yongqing Yang
author: Liuxiao Guo
published in: Abstract and Applied Analysis
publication date: 2014-06-23T00:00:00Z
review text: Summary: In this paper, the mean square asymptotic stability of stochastic Markovian jump neural networks with different time scales and randomly occurred nonlinearities is investigated. In terms of linear matrix inequality (LMI) approach and delay-partitioning projection technique, delay-dependent stability criteria are derived for the considered neural networks for cases with or without the information of the delay rates via new Lyapunov-Krasovskii functionals. We also obtain that the thinner the delay is partitioned, the more obviously the conservatism can be reduced. An example with simulation results is given to show the effectiveness of the proposed approach.
Mathematics Subject Classification ID: 60H10
Mathematics Subject Classification ID: 60J27
Mathematics Subject Classification ID: 34K20
Mathematics Subject Classification ID: 34E13
Mathematics Subject Classification ID: 34K50
zbMATH DE Number: 6306534
Wikidata QID: Q58915568
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/212469
OpenAlex ID: W2156027217
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description: scientific article; zbMATH DE number 6306534