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Nonlinear System Identification for Mixed Signal Processing | Signal Processing and Speech Communication Laboratory

Koeppl, H. (2004):
Nonlinear System Identification for Mixed Signal Processing | Signal Processing and Speech Communication Laboratory.
Graz Universitay of Technology, Graz, Austria, [Online-Edition: http://www.spsc.tugraz.at/PhD_Theses/nonlinear-system-identi...],
[Ph.D. Thesis]

Abstract

The thesis considers methods for the identification of weakly nonlinear systems, met in mixed analog-digital systems for data-transmission. Depending on the available knowledge about the system to be identified different algorithms and model structures can be applied. Thus, one distinguishes between glass-box, gray-box and black-box methods. The contribution of the thesis to the glass-box methods is a scheme for the automatic determination of the Volterra kernels of a weakly nonlinear circuit utilizing Kronecker products. In the field of gray-box methods a model structure and its parameter estimation is presented that allow to incorporate the available knowledge about the linearization of the weakly nonlinear system efficiently into the identification. Black-box methods are extended through the application of model-complexity regulating algorithms from the area of machine learning. Furthermore the relation between the accuracy of the identification and properties of the excitation signal for the identification is investigated and a signal optimization method is proposed. The developed methods are presented using an exemplary circuit and are also applied to the identification of a VDSL (very-high data rate digital subscriber line) line driver circuit.

Item Type: Ph.D. Thesis
Erschienen: 2004
Creators: Koeppl, H.
Title: Nonlinear System Identification for Mixed Signal Processing | Signal Processing and Speech Communication Laboratory
Language: English
Abstract:

The thesis considers methods for the identification of weakly nonlinear systems, met in mixed analog-digital systems for data-transmission. Depending on the available knowledge about the system to be identified different algorithms and model structures can be applied. Thus, one distinguishes between glass-box, gray-box and black-box methods. The contribution of the thesis to the glass-box methods is a scheme for the automatic determination of the Volterra kernels of a weakly nonlinear circuit utilizing Kronecker products. In the field of gray-box methods a model structure and its parameter estimation is presented that allow to incorporate the available knowledge about the linearization of the weakly nonlinear system efficiently into the identification. Black-box methods are extended through the application of model-complexity regulating algorithms from the area of machine learning. Furthermore the relation between the accuracy of the identification and properties of the excitation signal for the identification is investigated and a signal optimization method is proposed. The developed methods are presented using an exemplary circuit and are also applied to the identification of a VDSL (very-high data rate digital subscriber line) line driver circuit.

Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Date Deposited: 04 Apr 2014 12:32
Official URL: http://www.spsc.tugraz.at/PhD_Theses/nonlinear-system-identi...
Refereed / Verteidigung / mdl. Prüfung: 2004
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