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dc.contributor.advisorMorgül, Ömeren_US
dc.contributor.authorYavuzer, Mahmuten_US
dc.date.accessioned2016-01-08T18:18:32Z
dc.date.available2016-01-08T18:18:32Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/11693/15442
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2010.en_US
dc.descriptionIncludes bibliographical references leaves 112-116.en_US
dc.description.abstractThe well-known Wiener and Hammerstein type nonlinear systems and their various combinations are frequently used both in the modeling and the control of various electrical, physical, biological, chemical, etc... systems. In this thesis we will concentrate on the parametric identification and control of these type of systems. In literature, various identification methods are proposed for the identification of Hammerstein and Wiener type of systems. Recently, Least Squares-Support Vector Machines (LS-SVM) are also applied in the identification of Hammerstein type systems. In the majority of these works, the nonlinear part of Hammerstein system is assumed to be algebraic, i.e. memoryless. In this thesis, by using LS-SVM we propose a method to identify Hammerstein systems where the nonlinear part has a finite memory. For the identification of Wiener type systems, although various methods are also available in the literature, one approach which is proposed in some works would be to use a method for the identification of Hammerstein type systems by changing the roles of input and output. Through some simulations it was observed that this approach may yield poor estimation results. Instead, by using LS-SVM we proposed a novel methodology for the identification of Wiener type systems. We also proposed various modifications of this methodology and utilized it for some control problems associated with Wiener type systems. We also proposed a novel methodology for identification of NARX (Nonlinear Auto-Regressive with eXogenous inputs) systems. We utilize LS-SVM in our methodology and we presented some results which indicate that our methodology may yield better results as compared to the Neural Network approximators and the usual Support Vector Regression (SVR) formulations. We also extended our methodology to the identification of Wiener-Hammerstein type systems. In many applications the orders of the filter, which represents the linear part of the Wiener and Hammerstein systems, are assumed to be known. Based on LS-SVR, we proposed a methodology to estimate true ordersen_US
dc.description.statementofresponsibilityYavuzer, Mahmuten_US
dc.format.extentxiii, 116 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSystem Identificationen_US
dc.subjectWiener Systemsen_US
dc.subjectHammerstein Systemsen_US
dc.subjectWiener-Hammerstein Systemsen_US
dc.subjectNonlinear Auto-Regressive with eXogenous inputs (NARX)en_US
dc.subjectLeast-Squares Support Vector Machines (LS-SVM)en_US
dc.subjectLeast-Squares Support Vector Regression (LS-SVR)en_US
dc.subjectControlen_US
dc.subject.lccQA402.35 .Y38 2010en_US
dc.subject.lcshNonlinear theories.en_US
dc.subject.lcshNonlinear systems--Mathematical models.en_US
dc.subject.lcshSystem identification.en_US
dc.subject.lcshControl theory.en_US
dc.subject.lcshSystem analysis.en_US
dc.titleIdentification of some nonlinear systems by using least-squares support vector machinesen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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