dc.contributor.advisor | Morgül, Ömer | |
dc.contributor.author | Yavuzer, Mahmut | |
dc.date.accessioned | 2016-01-08T18:18:32Z | |
dc.date.available | 2016-01-08T18:18:32Z | |
dc.date.issued | 2010 | |
dc.identifier.uri | http://hdl.handle.net/11693/15442 | |
dc.description | Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010. | en_US |
dc.description | Thesis (Master's) -- Bilkent University, 2010. | en_US |
dc.description | Includes bibliographical references leaves 112-116. | en_US |
dc.description.abstract | The 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 orders | en_US |
dc.description.statementofresponsibility | Yavuzer, Mahmut | en_US |
dc.format.extent | xiii, 116 leaves, illustrations | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | System Identification | en_US |
dc.subject | Wiener Systems | en_US |
dc.subject | Hammerstein Systems | en_US |
dc.subject | Wiener-Hammerstein Systems | en_US |
dc.subject | Nonlinear Auto-Regressive with eXogenous inputs (NARX) | en_US |
dc.subject | Least-Squares Support Vector Machines (LS-SVM) | en_US |
dc.subject | Least-Squares Support Vector Regression (LS-SVR) | en_US |
dc.subject | Control | en_US |
dc.subject.lcc | QA402.35 .Y38 2010 | en_US |
dc.subject.lcsh | Nonlinear theories. | en_US |
dc.subject.lcsh | Nonlinear systems--Mathematical models. | en_US |
dc.subject.lcsh | System identification. | en_US |
dc.subject.lcsh | Control theory. | en_US |
dc.subject.lcsh | System analysis. | en_US |
dc.title | Identification of some nonlinear systems by using least-squares support vector machines | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | M.S. | en_US |