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  State estimation of fractional-order delayed memristive neural networks

Bao, H., Cao, J., Kurths, J. (2018): State estimation of fractional-order delayed memristive neural networks. - Nonlinear Dynamics, 94, 2, 1215-1225.
https://doi.org/10.1007/s11071-018-4419-3

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Bao, H.1, Author
Cao, J.1, Author
Kurths, Jürgen2, Author              
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: This paper focuses on designing state estimators for fractional-order memristive neural networks (FMNNs) with time delays. It is meaningful to propose a suitable state estimator for FMNNs because of the following two reasons: (1) different initial conditions of memristive neural networks (MNNs) may cause parameter mismatch; (2) state estimation approaches and theories for integer-order neural networks cannot be directly extended and used to deal with fractional-order neural networks. The present paper first investigates state estimation problem for FMNNs. By means of Lyapunov functionals and fractional-order Lyapunov methods, sufficient conditions are built to ensure that the estimation error system is asymptotically stable, which are readily solved by MATLAB LMI Toolbox. Ultimately, two examples are presented to show the effectiveness of the proposed theorems.

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 Dates: 2018
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s11071-018-4419-3
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 8301
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
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Title: Nonlinear Dynamics
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 94 (2) Sequence Number: - Start / End Page: 1215 - 1225 Identifier: Other: 1573-269X
ISSN: 0924-090X
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nonlinear-dynamics
Publisher: Springer