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dc.contributor.authorLiu, Dayan*
dc.contributor.authorGibaru, O.*
dc.contributor.authorPerruquetti, Wilfrid*
dc.date.accessioned2015-08-04T07:04:32Zen
dc.date.available2015-08-04T07:04:32Zen
dc.date.issued2012-08en
dc.identifier.isbn9781467306720en
dc.identifier.doi10.1109/IConSCS.2012.6502445en
dc.identifier.urihttp://hdl.handle.net/10754/564585en
dc.description.abstractRecently, Mboup, Join and Fliess [27], [28] introduced non-asymptotic integer order differentiators by using an algebraic parametric estimation method [7], [8]. In this paper, in order to obtain non-asymptotic fractional order differentiators we apply this algebraic parametric method to truncated expansions of fractional Taylor series based on the Jumarie's modified Riemann-Liouville derivative [14]. Exact and simple formulae for these differentiators are given where a sliding integration window of a noisy signal involving Jacobi polynomials is used without complex mathematical deduction. The efficiency and the stability with respect to corrupting noises of the proposed fractional order differentiators are shown in numerical simulations. © 2012 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://arxiv.org/abs/arXiv:1207.0129v1en
dc.titleNon-asymptotic fractional order differentiators via an algebraic parametric methoden
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division*
dc.identifier.journal2012 1st International Conference on Systems and Computer Science (ICSCS)en
dc.conference.date29 August 2012 through 30 August 2012en
dc.conference.name2012 1st International Conference on Systems and Computer Science, ICSCS 2012en
dc.conference.locationVilleneuve d'Ascqen
dc.contributor.institutionLSIS (CNRS, UMR 7296), Arts et Métiers ParisTech, Centre de Lille, France*
dc.contributor.institutionLAGIS (CNRS, UMR 8146), École Centrale de Lille, INRIA Lille-Nord Europe, France*
dc.identifier.arxividarXiv:1207.0129en
kaust.authorLiu, Dayan*


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