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Dissertations / Theses on the topic 'Nonlinear system identificatio'

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Published: 4 June 2025

Last updated: 1 August 2025

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1

Mathew, Manu K. "Nonlinear system identification and prediction /." Online version of thesis, 1993. http://hdl.handle.net/1850/11594.

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2

Ziegler, Edward H. "Nonlinear system identification /." Online version of thesis, 1994. http://hdl.handle.net/1850/11583.

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3

Arain, Muhammad Asif, Ayala Helon Vicente Hultmann, and Muhammad Adil Ansari. "Nonlinear System Identification Using Neural Network." University of Genova (Italy) and Warsaw University of Technology (Poland), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-28937.

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Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.

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4

Brunke, Shelby Scott. "Nonlinear filtering and system identification algorithms for autonomous systems /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/7095.

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5

Chow, Po-Chuan. "Recursive nonlinear identification of Hammerstein-type systems." Case Western Reserve University School of Graduate Studies / OhioLINK, 1990. http://rave.ohiolink.edu/etdc/view?acc_num=case1054758543.

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6

Mattsson, Per. "Modeling and identification of nonlinear and impulsive systems." Doctoral thesis, Uppsala universitet, Avdelningen för systemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-304837.

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Mathematical modeling of dynamical systems plays a central roll in science and engineering. This thesis is concerned with the process of finding a mathematical model, and it is divided into two parts - one that concentrates on nonlinear system identification and another one where an impulsive model of testosterone regulation is constructed and analyzed. In the first part of the thesis, a new latent variable framework for identification of a large class of nonlinear models is developed. In this framework, we begin by modeling the errors of a nominal predictor using a flexible stochastic model.

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7

Lee, James X. "On fuzzy logic systems, nonlinear system identification, and adaptive control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq26881.pdf.

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8

Tayamon, Soma. "Nonlinear system identification with applications to selective catalytic reduction systems." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-186963.

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The stringent regulations on the emissions levels of heavy duty vehicles create a demand for new methods of reducing harmful emissions from the engine. In order to be able to follow these increasingly stricter legislations, complex aftertreatment systems are used. Achievement of optimal performance of these systems requires accurate models that can be used for control design. As a result, the interest in modelling and control of aftertreatment systems has increased. This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavy duty vehicles using the selective catalyst

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9

Lee, James X. (James Xiang) Carleton University Dissertation Engineering Mechanical and Aerospace. "On fuzzy logic systems, nonlinear system identification, and adaptive control." Ottawa, 1997.

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10

Li, Liangmin. "Continuous time nonlinear system identification." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341867.

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11

Haider, Usama. "Smart Maintenance using System Identification." Thesis, Högskolan i Gävle, Avdelningen för elektroteknik, matematik och naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-30735.

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This project discusses the use of System Identification for Smart Maintenance. System Identification is the process of finding a mathematical model of a system using empirical data. The mathematical model can then be used to detect and predict the maintenance needs, which is considered as Smart Maintenance. Smart maintenance strategies have gained pretty much importance recently, since it contributes to economically sustainable production. This project uses the LAVA-framework, proposed in [1] for non-linear system identification, which has the capability of explaining the dynamics of the syste

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12

Brus, Linda. "Nonlinear Identification and Control with Solar Energy Applications." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8594.

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13

Tayamon, Soma. "Nonlinear System Identification and Control Applied to Selective Catalytic Reduction Systems." Doctoral thesis, Uppsala universitet, Avdelningen för systemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-229148.

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The stringent regulations of emission levels from heavy duty vehicles create a demand for new methods for reducing harmful emissions from diesel engines. This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavy duty vehicles using a selective catalyst as an aftertreatment system, utilising ammonia (NH3) for its reduction. The process of the selective catalytic reduction (SCR) is nonlinear, since the result of the chemical reactions involved depends on the load operating point and the temperature. The purpose of this thesis is to investigate different methods for no

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14

Zhang, Jin. "Identification of nonlinear structural dynamical system." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/12270.

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15

Kukreja, Sunil L. "Parametric methods for nonlinear system identification." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=37752.

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In this thesis, we have developed practical methods for the identification of linear, nonlinear and hybrid (multimode) systems which are applicable under relatively general conditions, i.e., when assumptions and conditions of the estimation technique are not violated. Since these algorithms were not designed specifically with any system(s) in mind, they should be applicable to experiments on a variety of systems in many different disciplines.<br>Results demonstrate that the (polynomial) NARMAX (Nonlinear Autoregressive, Moving Average eXogenous) model class is useful for modeling the input-out

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16

Harte, James Michael. "Nonlinear system identification for otoacoustic emissions." Thesis, University of Southampton, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402242.

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17

Cheng, Chi-hao. "Nonlinear communication system identification and compensation /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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18

Krauss, Ryan. "Experimental Identification of Nonlinear Systems." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36912.

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A procedure is presented for using a primary resonance excitation in experimentally identifying the nonlinear parameters of a model approximating the response of a cantilevered beam by a single mode. The model accounts for cubic inertia and stiffness nonlinearities and quadratic damping. The method of multiple scales is used to determine the frequency-response function for the system. Experimental frequency- and amplitude-sweep data are compared with the prediction of the frequency-response function in a least-squares curve-fitting algorithm. The algorithm is improved by maki

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19

Pepona, Eleni. "Identification of nonlinear interconnected systems." Thesis, Brunel University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540757.

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In this work we address the problem of identifying a discrete-time nonlinear system composed of a linear dynamical system connected to a static nonlinear component. We use linear fractional representation to provide a united framework for the identification of two classes of such systems. The first class consists of discrete-time systems consists of a linear time invariant system connected to a continuous nonlinear static component. The identification problem of estimating the unknown parameters of the linear system and simultaneously fitting a math order spline to the nonlinear data is addres

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20

Meira, Anrafel Silva. "Identificação não linear de um manipulador eletromecânico de três graus de liberdade." Universidade Federal da Paraíba, 2014. http://tede.biblioteca.ufpb.br:8080/handle/tede/7571.

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Submitted by Maria Suzana Diniz (msuzanad@hotmail.com) on 2015-11-10T13:06:52Z No. of bitstreams: 1 arquivototal.pdf: 2081019 bytes, checksum: 810ebb6d466319b898b20b865caa5d4f (MD5)<br>Made available in DSpace on 2015-11-10T13:06:52Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2081019 bytes, checksum: 810ebb6d466319b898b20b865caa5d4f (MD5) Previous issue date: 2014-12-12<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES<br>This paper presents multivariable nonlinear mathematical models with estimable parameters in online identification for an electromechanical mani

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21

Schooling, Steven Paul. "Structural determination in the development of nonlinear process models." Thesis, Manchester Metropolitan University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337272.

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22

Skantze, Fredrik Petter. "Model-based algorithms for nonlinear system identification." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/39749.

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23

KARIMSHOUSHTARI, MILAD. "Design of Experiments for Nonlinear System Identification." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2751496.

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24

Zhu, Quan Min. "Identification & control of nonlinear systems." Thesis, University of Warwick, 1989. http://wrap.warwick.ac.uk/62723/.

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This thesis investigates some problems on nonlinear system identification, parameter estimation, and signal processing. Random signal spectral analysis and system frequency response estimation are studied from incomplete time series. Both recursive and direct estimators are presented based on either an unbiased or minimum mean square error criterion. Nonlinear system identification and parameter estimation are studied. A quantisation technique is developed to give a clear geometrical interpretation for structure detection and parameter estimation. A new concept, state amplitude distance betwee

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25

Novák, Antonín. "Identification of nonlinear systems in acoustics." Le Mans, 2009. http://cyberdoc.univ-lemans.fr/theses/2009/2009LEMA1009.pdf.

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La théorie des systèmes linéaires invariant a fait l’objet de nombreuses études au cours de ces dernières décennies, et l’estimation d’un tel système à partir du signal de sortie lorsque le signal d’entrée est connu est un problème aujourd’hui résolu. Cependant, le comportement de tout système réel est plus ou moins non-linéaire. Dans le cas de faibles non-linéarités, une approximation linéaire peut être effectuée, mais lorsque les non-linéarités sont plus importantes, cette approximation linéaire n’est plus valide et il est nécessaire d’utiliser une représentation non-linéaire. L’objet de ce

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26

Reyland, John M. "Towards Wiener system identification with minimum a priori information." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/1066.

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The ability to construct accurate mathematical models of real systems is an important part of control systems design. A block oriented systems identification approach models the unknown system as interconnected linear and nonlinear blocks. The subject of this thesis is a particular configuration of these blocks referred to as a Wiener model. The Wiener model studied here is a cascade of a one input linear block followed by a nonlinear block which then provides one output. We assume that the signal between the linear and nonlinear block is always unknown, only the Wiener model input and output

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27

Markusson, Ola. "Model and System Inversion with Applications in Nonlinear System Identification and Control." Doctoral thesis, KTH, Signals, Sensors and Systems, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3287.

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28

Enqvist, Martin. "Linear Models of Nonlinear Systems." Doctoral thesis, Linköping : Linköpings universitet, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5330.

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29

McLoone, Seamus Cornelius. "Nonlinear identification using local model networks." Thesis, Queen's University Belfast, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326349.

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30

Myklebust, Andreas. "Closed Loop System Identification of a Torsion System." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17531.

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<p>A model is developed for the Quanser torsion system available at Control Systems Research Laboratory at Chulalongkorn University. The torsion system is a laboratory equipment that is designed for the study of position control. It consists of a DC motor that drives three inertial loads that are coupled in series with the motor, and where all components are coupled to each other through torsional springs.</p><p>Several nonlinearities are observed and the most significant one is an offset in the input signal, which is compensated for. Experiments are carried out under feedback as the system is

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31

Fung, Jimmy Jr. "Parameter Identification of Nonlinear Systems Using Perturbation Methods and Higher-Order Statistics." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36921.

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A parametric identification procedure is proposed that combines the method of multiple scales and higher-order statistics to efficiently and accurately model nonlinear systems. A theoretical background for the method of multiple scales and higher-order statistics is given. Validation of the procedure is performed through applying it to numerical simulations of two nonlinear systems. The results show how the procedure can successfully characterize the system damping and nonlinearities and determine the corresponding parameters. The procedure is then applied to experimental measur

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32

Alshejari, Abeer. "Neuro-fuzzy based intelligent approaches to nonlinear system identification and forecasting." Thesis, University of Westminster, 2018. https://westminsterresearch.westminster.ac.uk/item/q5w11/neuro-fuzzy-based-intelligent-approaches-to-nonlinear-system-identification-and-forecasting.

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Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or

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33

Wong, Chian Xng. "Nonlinear system identification with emphasis on dry friction." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425213.

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34

Chen, Yi S. M. Massachusetts Institute of Technology. "Nonlinear stochastic system identification techniques for biological tissues/." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62122.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 205-212).<br>This research develops a device capable of measuring the nonlinear dynamic mechanical properties of human tissue in vivo. The enabling technology is the use of nonlinear stochastic system identification techniques in conjunction with a high bandwidth actuator to perturb the tissue. The desktop and handheld instruments used for this investigation were custom-built Lorentz force actuators which were able to m

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35

Li, Kang. "Advanced methods for nonlinear system modelling and identification." Thesis, Queen's University Belfast, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.679232.

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System modelling and identification has played a key role in modern scientific research for system analysis, control and automation in many areas. This dissertation details the technical contributions of the applicant on parametric non linear system modelling and identification in the past 17 years since he joined Queen's University Belfast in 1998. The thesis first introduces the early research of the applicant on modelling the pollution emissions from fossil fuel fired thermal power plants for the purpose of reducing their negative environmental effects through advanced control. This work le

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36

Eichas, Felix [Verfasser]. "System Identification of Nonlinear Audio Circuits / Felix Eichas." Hamburg : Helmut-Schmidt-Universität, Bibliothek, 2020. http://d-nb.info/1212811860/34.

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37

Cooper, Matthew. "Bayesian system identification for nonlinear dynamical vehicle models." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213212/1/Matthew_Cooper_Thesis.pdf.

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This thesis investigates the use of novel Bayesian system identification techniques to estimate unknown parameters in nonlinear vehicle dynamics. In the first part of this thesis, a dual merging particle filter is proposed that accurately estimates non-Gaussian posterior parameter distributions for different vehicle models. In the second part of this thesis, a novel myopic sequential technique is proposed to design informative experiments for estimating the unknown parameters of a real-world robotic vehicle. This myopic technique is extended in the last part of the thesis to incorporate a roll

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38

Alegria, Elvis Omar Jara 1986. "Estimação On-Line de parâmetros dependentes do estado (State Dependent Parameter - SDP) em modelos de regressão não lineares." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/258834.

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Orientador: Celso Pascoli Bottura<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-27T02:15:32Z (GMT). No. of bitstreams: 1 Alegria_ElvisOmarJara_M.pdf: 5581682 bytes, checksum: cd5b08b04c7ba4bcd505ab00e5335ffc (MD5) Previous issue date: 2015<br>Resumo: Este trabalho é sobre a identificação recursiva em tempo real das dependências parâmetro-estado em modelos de regressão de series temporais estocásticas. O descobrimento dessas dependências é útil para obter uma nova, e mais acurada, estrutur

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39

Mao, Ke Zhi. "Nonlinear systems identification using the Narmax method." Thesis, University of Sheffield, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310424.

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40

Normann, James Brian. "Parametric identification of nonlinear structural dynamic systems." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/43294.

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The identification of linear structural dynamic systems has been dealt with extensively in past studies. Identification methods for nonlinear structures have also been introduced in previous articles, including procedures based on the method of multiple scales, iterative and noniterative direct methods, and state space mappings. Here, a procedure is introduced for the identification of nonlinear structural dynamic systems which is readily applicable to simple as well as more complex multiple degree of freedom systems. The procedure is based on multiple step integration methods for the solution

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41

Allison, Timothy Charles. "System Identification via the Proper Orthogonal Decomposition." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/29424.

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Although the finite element method is often applied to analyze the dynamics of structures, its application to large, complex structures can be time-consuming and errors in the modeling process may negatively affect the accuracy of analyses based on the model. System identification techniques attempt to circumvent these problems by using experimental response data to characterize or identify a system. However, identification of structures that are time-varying or nonlinear is problematic because the available methods generally require prior understanding about the equations of motion for the sy

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42

Zaidi, Salman [Verfasser]. "System Identification of Stochastic Nonlinear Dynamic Systems using Takagi-Sugeno Fuzzy Models / Salman Zaidi." Kassel : Kassel University Press, 2019. http://d-nb.info/118450279X/34.

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43

Gransten, Johan. "Linear and Nonlinear Identification of Solid Fuel Furnace." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5182.

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<p>The aim of this thesis is to develop the knowledge about nonlinear and/or adaptive solid fuel boiler control at Vattenfall Utveckling AB. The aim is also to make a study of implemented and published control strategies.</p><p>A solid fuel boiler is a large-scale heat (and power) generating plant. The Idbäcken boiler studied in this work, is a one hundred MW furnace mainly fired with wood chips. The control system consists of several linear PID controllers working together, and the furnace is a nonlinear system. That, and the fact that the fuel-flow is not monitored, are the main reasons for

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44

Xi, Zhiyu Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Identification and control of nonlinear laboratory processes." Awarded by:University of New South Wales. Electrical Engineering & Telecommunications, 2007. http://handle.unsw.edu.au/1959.4/40461.

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In this thesis, a class of control and identification methods on a typical laboratory process - a ball and beam system - are discussed. The ball and beam is a common laboratory process which contains nonlinearity, a double integrator and time-delay. In our project, the hardware made by Wincon (Quanser SRV02 +BB01) is used. The main contribution of this work is the development of a variety of controller design methods, which together with suitable parameter identification techniques provide tools for rapid prototyping for real time control of processes within the laboratory, in preparation for

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45

Weiss, Michael. "Frequency domain identification of block-oriented nonlinear systems." Thesis, University of South Wales, 2003. https://pure.southwales.ac.uk/en/studentthesis/frequency-domain-identification-of-blockoriented-nonlinear-systems(314dc35f-b29a-4ac9-825f-9a0281d5e2e2).html.

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The science of modelling the behaviour of natural phenomena and physical systems has grown significantly in importance in the last century. It helps us to understand and predict natural phenomena or improve and control all types of industrial processes. There are basically two approaches to system modelling: a model can be derived from physical knowledge of the system or by systematically testing it and estimating the model structure and parameters based on the test data. The method of system testing has gained importance due to the increasing complexity of modern industrial systems and proces

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46

Sze, Tiam Lin. "System identification using radial basis function networks." Thesis, University of Sheffield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364232.

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47

Josefsson, Andreas. "Identification and Simulation Methods for Nonlinear Mechanical Systems Subjected to Stochastic Excitation." Doctoral thesis, Karlskrona : Blekinge Institute of Technology, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00507.

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With an ongoing desire to improve product performance, in combination with the continuously growing complexity of engineering structures, there is a need for well-tested and reliable engineering tools that can aid the decision making and facilitate an efficient and effective product development. The technical assessment of the dynamic characteristics of mechanical systems often relies on linear analysis techniques which are well developed and generally accepted. However, sometimes the errors due to linearization are too large to be acceptable, making it necessary to take nonlinear effects into

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48

Mihaliuk, Eugene. "Identification and control of dynamical systems." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=965.

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49

Chan, Chee Keong. "Langrange programming neural networks for nonlinear Volterra system identification." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266354.

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50

David, Robert 1977. "Applications of nonlinear system identification to protein structural prediction." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/89270.

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