Dynamical Behaviors of Impulsive Stochastic Reaction-Diffusion Neural Networks with Mixed Time Delays
We discuss the dynamical behaviors of impulsive stochastic reaction-diffusion neural networks (ISRDNNs) with mixed time delays. By using a well-known L-operator differential inequality with mixed time delays and combining with the Lyapunov-Krasovkii functional approach, as well as linear matrix ineq...
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Series: | Abstract and Applied Analysis |
Online Access: | http://dx.doi.org/10.1155/2012/236562 |
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doaj-f210070abc974ea08d53803f90850bdc2020-11-24T23:49:20ZengHindawi LimitedAbstract and Applied Analysis1085-33751687-04092012-01-01201210.1155/2012/236562236562Dynamical Behaviors of Impulsive Stochastic Reaction-Diffusion Neural Networks with Mixed Time DelaysWeiyuan Zhang0Junmin Li1Minglai Chen2School of Science, Xidian University, Xi'an 710071, ChinaSchool of Science, Xidian University, Xi'an 710071, ChinaSchool of Science, Xidian University, Xi'an 710071, ChinaWe discuss the dynamical behaviors of impulsive stochastic reaction-diffusion neural networks (ISRDNNs) with mixed time delays. By using a well-known L-operator differential inequality with mixed time delays and combining with the Lyapunov-Krasovkii functional approach, as well as linear matrix inequality (LMI) technique, some novel sufficient conditions are derived to ensure the existence, uniqueness, and global exponential stability of the periodic solutions for ISRDNNs with mixed time delays in the mean square sense. The obtained sufficient conditions depend on the reaction-diffusion terms. The results of this paper are new and improve some of the previously known results. The proposed model is quite general since many factors such as noise perturbations, impulsive phenomena, and mixed time delays are considered. Finally, two numerical examples are provided to verify the usefulness of the obtained results.http://dx.doi.org/10.1155/2012/236562 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Weiyuan Zhang Junmin Li Minglai Chen |
spellingShingle |
Weiyuan Zhang Junmin Li Minglai Chen Dynamical Behaviors of Impulsive Stochastic Reaction-Diffusion Neural Networks with Mixed Time Delays Abstract and Applied Analysis |
author_facet |
Weiyuan Zhang Junmin Li Minglai Chen |
author_sort |
Weiyuan Zhang |
title |
Dynamical Behaviors of Impulsive Stochastic Reaction-Diffusion Neural Networks with Mixed Time Delays |
title_short |
Dynamical Behaviors of Impulsive Stochastic Reaction-Diffusion Neural Networks with Mixed Time Delays |
title_full |
Dynamical Behaviors of Impulsive Stochastic Reaction-Diffusion Neural Networks with Mixed Time Delays |
title_fullStr |
Dynamical Behaviors of Impulsive Stochastic Reaction-Diffusion Neural Networks with Mixed Time Delays |
title_full_unstemmed |
Dynamical Behaviors of Impulsive Stochastic Reaction-Diffusion Neural Networks with Mixed Time Delays |
title_sort |
dynamical behaviors of impulsive stochastic reaction-diffusion neural networks with mixed time delays |
publisher |
Hindawi Limited |
series |
Abstract and Applied Analysis |
issn |
1085-3375 1687-0409 |
publishDate |
2012-01-01 |
description |
We discuss the dynamical behaviors of impulsive stochastic reaction-diffusion neural networks (ISRDNNs) with mixed time delays. By using a well-known L-operator differential inequality with mixed time delays and combining with the Lyapunov-Krasovkii functional approach, as well as linear matrix inequality (LMI) technique, some novel sufficient conditions are derived to ensure the existence, uniqueness, and global exponential stability of the periodic solutions for ISRDNNs with mixed time delays in the mean square sense. The obtained sufficient conditions depend on the reaction-diffusion terms. The results of this paper are new and improve some of the previously known results. The proposed model is quite general since many factors such as noise perturbations, impulsive phenomena, and mixed time delays are considered. Finally, two numerical examples are provided to verify the usefulness of the obtained results. |
url |
http://dx.doi.org/10.1155/2012/236562 |
work_keys_str_mv |
AT weiyuanzhang dynamicalbehaviorsofimpulsivestochasticreactiondiffusionneuralnetworkswithmixedtimedelays AT junminli dynamicalbehaviorsofimpulsivestochasticreactiondiffusionneuralnetworkswithmixedtimedelays AT minglaichen dynamicalbehaviorsofimpulsivestochasticreactiondiffusionneuralnetworkswithmixedtimedelays |
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1725482767172501504 |