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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

Stability analysis of stochastic reaction–diffusion neural networks with Markovian switching and time delays in the leakage terms

verfasst von: Cheng-De Zheng, Yue Zhang, Zhanshan Wang

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

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Abstract

This paper investigates a class of stochastic reaction–diffusion neural networks with both Markovian jumping parameters and time delays in the leakage terms. By using the Lyapunov functional method, linear matrix inequality approach and stochastic analysis technique, a novel sufficient condition is derived to ensure the stochastic stability of the networks in the mean square sense. The proposed results, which do not require the differentiability and monotonicity of the activation functions, can be easily checked via Matlab LMI Toolbox. Moreover, they indicate that the stability behavior of neural networks is very sensitive to the time delay in the leakage term. Finally, two numerical examples are given to demonstrate the effectiveness of our theoretical results.
Vorheriger Artikel Advances in neural network based learning
Nächster Artikel Robust stability of stochastic uncertain recurrent neural networks with Markovian jumping parameters and time-varying delays

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Metadaten
Titel
Stability analysis of stochastic reaction–diffusion neural networks with Markovian switching and time delays in the leakage terms
verfasst von
Cheng-De Zheng
Yue Zhang
Zhanshan Wang
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/s13042-013-0165-5

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