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International Journal of Machine Learning and Cybernetics
Published 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

Authors: M. Syed Ali, P. Balasubramaniam, Quanxin Zhu

Published in: International Journal of Machine Learning and Cybernetics | Issue 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.
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Metadata
Title
Stability of stochastic fuzzy BAM neural networks with discrete and distributed time-varying delays
Authors
M. Syed Ali
P. Balasubramaniam
Quanxin Zhu
Publication date
13-12-2014
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0320-7

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