Dynamical analysis of neural networks with time-varying delays using the LMI approach
Data(s) |
01/01/2015
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Resumo |
This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer |
Relação |
http://dro.deakin.edu.au/eserv/DU:30080750/lakshmanan-dynamicalanalysis-2015.pdf http://dro.deakin.edu.au/eserv/DU:30080750/lakshmanan-dynamicalanalysis-evid1-2015.pdf http://dro.deakin.edu.au/eserv/DU:30080750/lakshmanan-dynamicalanalysis-evid2-2015.pdf http://www.dx.doi.org/10.1007/978-3-319-26555-1_34 |
Direitos |
2015, Springer |
Palavras-Chave | #Science & Technology #Technology #Computer Science, Artificial Intelligence #Computer Science, Theory & Methods #Computer Science #Neural networks #Interval time-varying delay #Stability #Linear matrix inequality #STABILITY-CRITERIA #DEPENDENT STABILITY |
Tipo |
Conference Paper |