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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2017

13.12.2014 | Original Article

Stability of stochastic fuzzy BAM neural networks with discrete and distributed time-varying delays

verfasst von: M. Syed Ali, P. Balasubramaniam, Quanxin Zhu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2017

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Abstract

Among the various fuzzy models, the well-known Takagi–Sugeno (T–S) fuzzy model is recognized as a popular and powerful tool in approximating a complex nonlinear system. T–S model provides a fixed structure to some nonlinear systems and facilitates the analysis of the system. This paper deals with the global stability of stochastic bidirectional associative memory (BAM) neural networks with discrete and distributed time-varying delays which are represented by the T–S fuzzy models. The stability conditions are derived using Lyapunov–Krasovskii functional combined with the linear matrix inequality (LMI) techniques. Finally, numerical examples are given to demonstrate the correctness of the theoretical results.
Vorheriger Artikel n-Dimensional fuzzy hyperideals in semihyperrings
Nächster Artikel A graph optimization method for dimensionality reduction with pairwise constraints

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Metadaten
Titel
Stability of stochastic fuzzy BAM neural networks with discrete and distributed time-varying delays
verfasst von
M. Syed Ali
P. Balasubramaniam
Quanxin Zhu
Publikationsdatum
13.12.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0320-7

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