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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2014

01.02.2014 | Original Article

Robust stability of stochastic uncertain recurrent neural networks with Markovian jumping parameters and time-varying delays

verfasst von: M. Syed Ali

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2014

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Abstract

In this paper, stability of stochastic recurrent neural networks with Markovian jumping parameters and time-varying delays is considered. A novel linear matrix inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of Markovian jumping stochastic recurrent neural networks with norm bounded uncertainties and time-varying delays. To reflect the most dynamical behaviors of the system, both parameter uncertainties and stochastic disturbance are considered, where parameter uncertainties enter into all the system matrices, stochastic disturbances are given in the form of a Brownian motion. The stability conditions are derived using Lyapunov–Krasovskii approach, in combined with the LMI techniques. The delay-dependent stability condition is formulated, in which the restriction of the derivative of the time-varying delay should be 1 is removed. Finally, numerical examples are given to demonstrate the correctness of the theoretical results.
Vorheriger Artikel Stability analysis of stochastic reaction–diffusion neural networks with Markovian switching and time delays in the leakage terms
Nächster Artikel Robust exponential stability analysis for interval Cohen–Grossberg type BAM neural networks with mixed time delays

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Metadaten
Titel
Robust stability of stochastic uncertain recurrent neural networks with Markovian jumping parameters and time-varying delays
verfasst von
M. Syed Ali
Publikationsdatum
01.02.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0124-6

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