Open AccessDynamical Behaviors of Impulsive Stochastic Reaction-Diffusion Neural Networks with Mixed Time Delays
Author(s) -
Weiyuan Zhang,
Junmin Li,
Minglai Chen
Publication year - 2012
Publication title -
abstract and applied analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.228
H-Index - 56
eISSN - 1687-0409
pISSN - 1085-3375
DOI - 10.1155/2012/236562
Subject(s) - mathematics , uniqueness , exponential stability , linear matrix inequality , reaction–diffusion system , stability (learning theory) , artificial neural network , diffusion , lyapunov function , matrix (chemical analysis) , control theory (sociology) , mathematical analysis , mathematical optimization , computer science , nonlinear system , materials science , composite material , physics , control (management) , quantum mechanics , machine learning , artificial intelligence , thermodynamics
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
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