Improved results on robust exponential stability criteria for neutral-type delayed neural networks

Autor:	Abdulla Ismail, Magdi S. Mahmoud
Rok vydání:	2010
Předmět:	Computational Mathematics Compact space Exponential stability Artificial neural network Control theory Applied Mathematics Bounded function Interval (mathematics) Type (model theory) Stability (probability) Mathematics
Zdroj:	Applied Mathematics and Computation. 217:3011-3019
ISSN:	0096-3003
DOI:	10.1016/j.amc.2010.08.034
Popis:	In this paper, we investigate the problem of robust global exponential stability analysis for a class of neutral-type neural networks. The interval time-varying delays allow for both slow and fast time-varying delays. The values of the time-varying uncertain parameters are assumed to be bounded within given compact sets. Improved global exponential stability condition is derived by employing new Lyapunov–Krasovskii functional and the integral inequality. The developed nominal and robust stability criteria is delay-dependent and characterized by linear-matrix inequalities (LMIs). The developed results are less conservative than previous published ones in the literature, which are illustrated by representative numerical examples.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::629aa9fb9a4e1cf185c121ec3b7d16fd https://doi.org/10.1016/j.amc.2010.08.034 Zobrazit plný text záznamu Full Text from ScienceDirect