A Modified Neural Filtering Algorithm for Tracking of Chaotic Signals

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dc.authorid0000-0002-0619-549X
dc.contributor.authorMenguc, Engin Cemal
dc.contributor.authorAcir, Nurettin
dc.contributor.editorAlDabass, D
dc.contributor.editorOrsoni, A
dc.contributor.editorCant, R
dc.contributor.editorYunus, J
dc.contributor.editorIbrahim, Z
dc.contributor.editorSaad, I
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2014
dc.departmentNiğde ÖHÜ
dc.description16th UKSim-AMSS International Conference on Computer Modelling and Simulation (UKSim) -- MAR 26-28, 2014 -- Cambridge, ENGLAND
dc.description.abstractIn this study, a modified neural filtering algorithm is presented for tracking of chaotic signals. A multilayer neural network (MLNN) structure is used in proposed design as a nonlinear adaptive filtering tool. Initially, the MLNN is linearized using Taylor series expansion and then the weight vector update rule is designed by using Lyapunov stability theory (LST) to adaptively update the weights of the MLNN. The tracking capability of the proposed algorithm is improved by using adaptation gain rate parameter "a(k)" which is iteratively adjusted itself depending on sequential tracking errors rate. The tracking ability of the proposed algorithm is tested on two chaotic signals and compared with conventional algorithms. The simulation results have supported that the proposed neural filtering algorithm achieved better performance.
dc.description.sponsorshipUK Simulat Soc, Asia Modelling & Simulat Sect, IEEE Comp Soc, IEEE Reg 8, European Federat Simulat Soc, European Council Modelling & Simulat, Kingston Univ, Imperial Coll,, Norwegian Univ Sci & Technol, Nottingham Trent Univ, Univ Technol Malaysia, Univ Sci Malaysia, Univ Malaysia Pahang, Univ Malaysia Sabah, Univ Technol Mara, Univ Malaysia Perlis, IEEE UK & RI, IEEE Reg 10, Machine Intelligence Res Labs, IEEE
dc.identifier.doi10.1109/UKSim.2014.10
dc.identifier.endpage268
dc.identifier.isbn978-1-4799-4923-6
dc.identifier.issn2381-4772
dc.identifier.scopus2-s2.0-84926647110
dc.identifier.scopusqualityN/A
dc.identifier.startpage265
dc.identifier.urihttps://dx.doi.org/10.1109/UKSim.2014.10
dc.identifier.urihttps://hdl.handle.net/11480/4289
dc.identifier.wosWOS:000411854100049
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2014 UKSIM-AMSS 16TH INTERNATIONAL CONFERENCE ON COMPUTER MODELLING AND SIMULATION (UKSIM)
dc.relation.ispartofseriesUKSim International Conference on Computer Modelling and Simulation
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectLyapunov stability theory
dc.subjectneural filtering algorithm
dc.subjectnonlinear filtering
dc.subjectmultilayer neural network
dc.titleA Modified Neural Filtering Algorithm for Tracking of Chaotic Signals
dc.typeConference Object

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