Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses

Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses

A class of fuzzy neural networks (FNNs) with time-varying delays and impulses is investigated. With removing some restrictions on the amplification functions, a new differential inequality is established, which improves previouse criteria. Applying this differential inequality, a series of new and u...

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Main Authors: Kaihong Zhao, Liwenjing Wang, Juqing Liu
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2013/935491
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spelling doaj-3a5762c2fab44a7381c710f8b58fe64c2020-11-25T00:13:08ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/935491935491Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and ImpulsesKaihong Zhao0Liwenjing Wang1Juqing Liu2Center of Engineering Mathematics and Department of Applied Mathematics, Kunming University of Science and Technology, Kunming, Yunnan 650093, ChinaCenter of Engineering Mathematics and Department of Applied Mathematics, Kunming University of Science and Technology, Kunming, Yunnan 650093, ChinaDepartment of Mathematics, Yuxi Normal University, Yuxi, Yunnan 653100, ChinaA class of fuzzy neural networks (FNNs) with time-varying delays and impulses is investigated. With removing some restrictions on the amplification functions, a new differential inequality is established, which improves previouse criteria. Applying this differential inequality, a series of new and useful criteria are obtained to ensure the existence of global robust attracting and invariant sets for FNNs with time-varying delays and impulses. Our main results allow much broader application for fuzzy and impulsive neural networks with or without delays. An example is given to illustrate the effectiveness of our results.http://dx.doi.org/10.1155/2013/935491
collection DOAJ
language English
format Article
sources DOAJ
author Kaihong Zhao
Liwenjing Wang
Juqing Liu
spellingShingle Kaihong Zhao
Liwenjing Wang
Juqing Liu
Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses
Journal of Applied Mathematics
author_facet Kaihong Zhao
Liwenjing Wang
Juqing Liu
author_sort Kaihong Zhao
title Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses
title_short Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses
title_full Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses
title_fullStr Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses
title_full_unstemmed Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses
title_sort global robust attractive and invariant sets of fuzzy neural networks with delays and impulses
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2013-01-01
description A class of fuzzy neural networks (FNNs) with time-varying delays and impulses is investigated. With removing some restrictions on the amplification functions, a new differential inequality is established, which improves previouse criteria. Applying this differential inequality, a series of new and useful criteria are obtained to ensure the existence of global robust attracting and invariant sets for FNNs with time-varying delays and impulses. Our main results allow much broader application for fuzzy and impulsive neural networks with or without delays. An example is given to illustrate the effectiveness of our results.
url http://dx.doi.org/10.1155/2013/935491
work_keys_str_mv AT kaihongzhao globalrobustattractiveandinvariantsetsoffuzzyneuralnetworkswithdelaysandimpulses
AT liwenjingwang globalrobustattractiveandinvariantsetsoffuzzyneuralnetworkswithdelaysandimpulses
AT juqingliu globalrobustattractiveandinvariantsetsoffuzzyneuralnetworkswithdelaysandimpulses
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