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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 6/2017

12-07-2016 | Original Article

Relaxed exponential passivity criteria for memristor-based neural networks with leakage and time-varying delays

Authors: Jianying Xiao, Shouming Zhong, Yongtao Li, Fang Xu

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2017

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Abstract

This paper investigates the problem of exponential passivity analysis for memristive neural networks with leakage and time-varying delays. Given that the input and output of the considered neural networks satisfy a prescribed passivity-inequality constraint, the more relaxed criteria are established in terms of linear matrix inequalities by employing nonsmooth analysis and Lyapunov method. The relaxations lie in three aspects: first, this obtained criteria do not really require all the symmetric matrices involved in the employed quadratic Lyapunov-Krasovskii functional to be positive definite; second, the activation functions become general; third, the time-varying delay is not needed to be differentiable. Finally, two numerical examples are given to show the effectiveness of the proposed criteria.
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Metadata
Title
Relaxed exponential passivity criteria for memristor-based neural networks with leakage and time-varying delays
Authors
Jianying Xiao
Shouming Zhong
Yongtao Li
Fang Xu
Publication date
12-07-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0565-4

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