Globally exponential stabilization of neural networks with mixed time delays via impulsive control

Autor:	Wu-Hua Chen, Ganji Huang, Linna Wei
Rok vydání:	2015
Předmět:	Lyapunov function Time delays Artificial neural network Applied Mathematics Control (management) Linear matrix inequality Structure (category theory) State (functional analysis) Computational Mathematics symbols.namesake Exponential stability Control theory symbols Mathematics
Zdroj:	Applied Mathematics and Computation. 260:10-26
ISSN:	0096-3003
DOI:	10.1016/j.amc.2015.03.043
Popis:	The impulsive stabilization problem of neural networks with discrete time-varying delays and unbounded continuously distributed delays is considered. By using impulse-time-dependent Lyapunov function-based techniques to capture the hybrid structure characteristics of the considered impulsive neural networks, two novel global exponential stability criteria are obtained in terms of linear matrix inequalities, which are capable of dealing with the case where both the continuous and discrete dynamics are unstable. When the original continuous-time delayed neural networks are not stable, sufficient conditions are developed for the design of exponentially stable linear impulsive state feedback controllers. Four numerical examples are given to illustrate the less conservatism and effectiveness of the proposed results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::405f5baa416837fee25147e68127ea55 https://doi.org/10.1016/j.amc.2015.03.043 Zobrazit plný text záznamu Full Text from ScienceDirect