Doğrusal olmayan bazı sistemlerin en küçük kareli destek vektör makineleriyle tanılanması

The well-knownWiener and Hammerstein type nonlinear systems and their various combinations arefrequently 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 ofthese type of systems. In literature, various identification methods are proposed for the identificationof 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 ofthese 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 thenonlinear part has a finite memory. For the identification of Wiener type systems, although variousmethods are also available in the literature, one approach which is proposed in some works would beto use a method for the identification of Hammerstein type systems by changing the roles of inputand output. Through some simulations it was observed that this approach may yield poor estimationresults. Instead, by using LS-SVM we proposed a novel methodology for the identification ofWiener type systems. We also proposed various modifications of this methodology and utilized it forsome control problems associated with Wiener type systems. We also proposed a novel methodologyfor identification of NARX (Nonlinear Auto-Regressive with eXogenous inputs) systems. We utilizeLS-SVM in our methodology and we presented some results which indicate that our methodologymay yield better results as compared to the Neural Network approximators and the usual SupportVector Regression (SVR) formulations. We also extended our methodology to the identification ofWiener-Hammerstein type systems. In many applications the orders of the filter, which represents thelinear part of the Wiener and Hammerstein systems, are assumed to be known. Based on LS-SVR,we proposed a methodology to estimate true orders.Keywords: System Identification,Wiener Systems, Hammerstein Systems,Wiener-Hammerstein Systems,Nonlinear Auto-Regressive with eXogenous inputs (NARX), Least-Squares Support VectorMachines (LS-SVM), Least-Squares Support Vector Regression (LS-SVR), Control.