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  H-infinity state estimation of stochastic memristor-based neural networks with time-varying delays

Bao, H., Cao, J., Kurths, J., Alsaedi, A., Ahmad, B. (2018): H-infinity state estimation of stochastic memristor-based neural networks with time-varying delays. - Neutral Networks, 99, 79-91.
https://doi.org/10.1016/j.neunet.2017.12.014

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 Creators:
Bao, H.1, Author
Cao, J.1, Author
Kurths, Jürgen2, Author              
Alsaedi, A.1, Author
Ahmad, B.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: This paper addresses the problem of state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results.

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 Dates: 2018
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neunet.2017.12.014
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 8072
Research topic keyword: Complex Networks
Research topic keyword: Extremes
Research topic keyword: Nonlinear Dynamics
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Neutral Networks
Source Genre: Journal
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Pages: - Volume / Issue: 99 Sequence Number: - Start / End Page: 79 - 91 Identifier: Publisher: Elsevier