Exponential stabilization of non-autonomous delayed neural networks via Riccati equations
Data(s) |
01/11/2014
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Resumo |
This paper concerns with the problem of exponential stabilization for a class of non-autonomous neural networks with mixed discrete and distributed time-varying delays. Two cases of discrete time-varying delay, namely (i) slowly time-varying; and (ii) fast time-varying, are considered. By constructing an appropriate Lyapunov-Krasovskii functional in case (i) and utilizing the Razumikhin technique in case (ii), we establish some new delay-dependent conditions for designing a memoryless state feedback controller which stabilizes the system with an exponential convergence of the resulting closed-loop system. The proposed conditions are derived through solutions of some types of Riccati differential equations. Applications to control a class of autonomous neural networks with mixed time-varying delays are also discussed in this paper. Some numerical examples are provided to illustrate the effectiveness of the obtained results. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Elsevier |
Relação |
http://dro.deakin.edu.au/eserv/DU:30087337/hien-exponentialstabilization-2014.pdf http://www.dx.doi.org/10.1016/j.amc.2014.08.045 |
Direitos |
2014, Elsevier |
Palavras-Chave | #neural networks #stabilization #time-varying delays #lyapunov function #Matrix Riccati equations #linear matrix inequalities |
Tipo |
Journal Article |