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Neural Computing and Applications
Published in: Neural Computing and Applications 11/2018

28-03-2017 | Original Article

Further results on dissipativity analysis for Markovian jump neural networks with randomly occurring uncertainties and leakage delays

Authors: T. Radhika, G. Nagamani, Quanxin Zhu, S. Ramasamy, R. Saravanakumar

Published in: Neural Computing and Applications | Issue 11/2018

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Abstract

This paper is concerned with the mixed \(H_\infty\) and dissipativity performance for Markovian jump neural networks with time delay in the leakage term and randomly occurring uncertainties. The randomly occurring uncertainties are assumed to be mutually uncorrelated Bernoulli-distributed white noise sequences. By introducing a triple-integrable term in the Lyapunov functional, the Wirtinger-based double-integral inequality is utilized to bound the derivative of the triple-integral term and then a sufficient condition is derived to ensure that the considered neural networks to be strict \(({\mathcal {Q,S,R}}) - \gamma\)-dissipative and passive. These conditions are presented in terms of linear matrix inequalities, which can be easily solved by using standard numerical software. Finally, numerical examples are given to show the effectiveness and the potential significance of the proposed results.
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Metadata
Title
Further results on dissipativity analysis for Markovian jump neural networks with randomly occurring uncertainties and leakage delays
Authors
T. Radhika
G. Nagamani
Quanxin Zhu
S. Ramasamy
R. Saravanakumar
Publication date
28-03-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 11/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2942-7

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