Relevant bibliographies by topics / System of identification / Dissertations / Theses
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Dissertations / Theses on the topic 'System of identification'

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Published: 28 May 2022
Last updated: 30 July 2025

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1

Venkatesh, Saligrama Ramaswamy. "System-identification for complex-systems." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10440.

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2

Hof, Jacoba Marchiena van den. "System theory and system identification of compartmental systems." [S.l. : [Groningen] : s.n.] ; [University Library Groningen] [Host], 1996. http://irs.ub.rug.nl/ppn/152829512.

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3

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

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4

Luo, Dapeng. "SYSTEM IDENTIFICATION AND FAULT DETECTION OF COMPLEX SYSTEMS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3583.

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The proposed research is devoted to devising system identification and fault detection approaches and algorithms for a system characterized by nonlinear dynamics. Mathematical models of dynamical systems and fault models are built based on observed data from systems. In particular, we will focus on statistical subspace instrumental variable methods which allow the consideration of an appealing mathematical model in many control applications consisting of a nonlinear feedback system with nonlinearities at both inputs and outputs. Different solutions within the proposed framework are presented t
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Ziegler, Edward H. "Nonlinear system identification /." Online version of thesis, 1994. http://hdl.handle.net/1850/11583.

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6

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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7

Zhao, Liang. "System identification for complex financial system." Thesis, University of Sheffield, 2011. http://etheses.whiterose.ac.uk/14654/.

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The mam purpose of this thesis focuses on the investigation of major financial volatility models including the relevant mean model used in the context of volatility estimation, and the development of a systematic nonlinear identification methodology for these problems. Financial volatility is one of the key aspects in financial economics and volatility modelling involves both the mean process modelling, and the volatility process modelling. Although many volatility models have been derived to approximate the volatility process, linear mean models are almost always used and to the best of our k
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8

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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9

Wangpattanasirikul, Suthipong. "Port-based system identification." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/39769.

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10

Chen, Chun-Cheng Richard 1977. "Automated cardiovascular system identification." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/81537.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 64-65).<br>by Chun-Cheng Chen.<br>S.B.and M.Eng.
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11

Chung, Ka Kei. "Interactive visual optimization and analysis for RFID system performance /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20CHUNG.

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12

Lyzell, Christian. "Structural Reformulations in System Identification." Doctoral thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84515.

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In system identification, the choice of model structure is important and it is sometimes desirable to use a flexible model structure that is able to approximate a wide range of systems. One such model structure is the Wiener class of systems, that is, systems where the input enters a linear time-invariant subsystem followed by a time-invariant nonlinearity. Given a sequence of input and output pairs, the system identification problem is often formulated as the minimization of the mean-square prediction error. Here, the prediction error has a nonlinear dependence on the parameters of the linear
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13

Zhou, Ning. "Subspace methods of system identification applied to power systems." Laramie, Wyo. : University of Wyoming, 2005. http://proquest.umi.com/pqdweb?did=1095432761&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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14

Brunot, Mathieu. "Identification of rigid industrial robots - A system identification perspective." Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/20776/1/BRUNOT_Mathieu_20776.pdf.

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In modern manufacturing, industrial robots are essential components that allow saving cost, increase quality and productivity for instance. To achieve such goals, high accuracy and speed are simultaneously required. The design of control laws compliant with such requirements demands high-fidelity mathematical models of those robots. For this purpose, dynamic models are built from experimental data. The main objective of this thesis is thus to provide robotic engineers with automatic tools for identifying dynamic models of industrial robot arms. To achieve this aim, a comparative analysis of th
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15

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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16

Parker, Gregory K. "Heat transfer parametric system identification." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA268525.

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17

Lyzell, Christian. "Initialization Methods for System Identification." Licentiate thesis, Linköping University, Linköping University, Automatic Control, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51688.

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<p>In the system identification community a popular framework for the problem of estimating a parametrized model structure given a sequence of input and output pairs is given by the prediction-error method. This method tries to find the parameters which maximize the prediction capability of the corresponding model via the minimization of some chosen cost function that depends on the prediction error. This optimization problem is often quite complex with several local minima and is commonly solved using a local search algorithm. Thus, it is important to find a good initial estimate for the loca
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18

Zhang, Jing 1961. "System identification of bladder hydrodynamics." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=26440.

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Understanding bladder mechanics and the changes caused by bladder outlet obstruction is an important task in urology. In this work, bladder mechanics are examined in terms of bladder hydrodynamics: the relation between a perturbing volume applied to the bladder and the evoked pressure change. A PC-based experimental system was built which can generate a computer-controlled perturbation volume and measure volume and pressure signals.<br>The bladders of six minipigs, three normal and three obstructed, were subjected to stochastic volume perturbations about different average volume levels and evo
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19

AlHilal, M. H. "System identification using pseudorandom signals." Thesis, Swansea University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635747.

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20

Dal, Santo Paul S. "System identification by ARMA modeling." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/23417.

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System identification concerns the mathematical modeling of a system based upon its input and output. It allows the development of a mathematical description when all that is available is the result of a process or the output of a system and not the process or system itself. The purpose of this thesis is to develop algorithms for modeling systems as autoregressive-moving-average processes using the method of instrumental variables, a modification of the ordinary least-squares technique, and a multichannel method based upon processing the input and output data by separate infinite-response filt
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21

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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22

Bjurgert, Johan. "System Identification by Adaptive Boosting." Thesis, KTH, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179711.

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In the field of machine learning, the algorithm Adaptive Boosting has beensuccessfully applied to a wide range of regression and classification problems.Still, there is no known method to use the algorithm to estimate dynamical systems.In this thesis, the relationship between Adaptive Boosting and systemidentification is explored. A new identification method, inspired by AdaptiveBoosting, called TM-Boost is introduced. It fits a dynamical model byiteratively adding orthonormal basis functions. An interesting feature of themethod is that there is no need to specify a model order. It is also pro
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23

Cooper, David L. "Continuous-time multivariable system identification." Thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/42098.

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24

Mohammedshah, Juzer Mohsin. "System identification of adaptive composites." Thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/41935.

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25

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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26

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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27

Szabo, Andrew P. "System Identification and Model-Based Control of Quadcopter UAVs." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1553197265058507.

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28

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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29

Dautbegovic, Dino. "Convex Optimization Methods for System Identification." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34808.

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The extensive use of a least-squares problem formulation in many fields is partly motivated by the existence of an analytic solution formula which makes the theory comprehensible and readily applicable, but also easily embedded in computer-aided design or analysis tools. While the mathematics behind convex optimization has been studied for about a century, several recent researches have stimulated a new interest in the topic. Convex optimization, being a special class of mathematical optimization problems, can be considered as generalization of both least-squares and linear programming. As in
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30

Anderson, Jeremiah P. "State-space modeling, system identification and control of a 4th order rotational mechanical system." Thesis, Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Dec/09Dec%5FAnderson.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2009.<br>Thesis Advisor(s): Yun, Xiaoping. Second Reader: Julian, Alex. "December 2009." Description based on title screen as viewed on January 26, 2010. Author(s) subject terms: System identification, state-space, pole placement, full state feedback, observer. Includes bibliographical references (p. 91). Also available in print.
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31

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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32

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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33

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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34

González, Rodrigo A. "Consistency and efficiency in continuous-time system identification." Licentiate thesis, KTH, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273176.

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Continuous-time system identification deals with the problem of building continuous-time models of dynamical systems from sampled input and output data. In this field, there are two main approaches: indirect and direct. In the indirect approach, a suitable discrete-time model is first determined, and then it is transformed into continuous-time. On the other hand, the direct approach obtains a continuous-time model directly from the sampled data. In both approaches there exists a dichotomy between discrete-time data and continuous-time models, which can induce robustness issues and complication
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35

Jarny, Yvon. "Identification de modeles non-lineaires de systemes thermiques." Nantes, 1987. http://www.theses.fr/1987NANT2048.

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Utilisation d'une methode de modelisation due a g. Chavent, consistant a minimiser un critere de moindres carres de sortie. Etude de 3 modeles comportant des parametres variables en fonction de l'etat du systeme ou du temps. Le probleme d'identification est formule comme un probleme d'optimatisation et resolu numeriquement par un algorithme de descente
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36

Feng, Tianyang, and You Zhou. "Disturbing Sound Cancellation Using System Identification." Thesis, University of Gävle, Department of Electronics, Mathematics and Natural Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-7317.

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<p>Disturbing sound sometimes should be cancelled when music has been recorded. In this thesis, MATLAB was used as a tool. System identification was a main method used to find the unknown system. By subtracting the simulated output, disturbing sound was cancelled. Two different systems were identified with both linear (ARX) model and nonlinear (Parallel Hammerstein) model. The quality of these models was measured and compared using different methods. Possibility to implement this work on hardware was also discussed.</p>
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37

Ingebretsen, Thomas. "System Identification of Unmanned Aerial Vehicles." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for fysikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-16776.

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The least squares method has been applied to estimate parameters inan aerodynamic model of a simulated aircraft, using data that can beexpected to available from sensors on an Unmanned Aerial Vehicle. Acombination of two non-linear state observers have been implemented toestimate wind data such as angle of attack, sideslip and dynamic pressure.Simulations have confirmed that the observers are able to estimete thewind data using noisy sensor measurements. Parameter estimation havebeen demonstrated with both measured and estimated wind data.
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38

Wester, Stefan. "Dynamic system identification of a strainfield." Thesis, KTH, Reglerteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105153.

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To be able to monitor forces acting on objects is important for a lot of dierent applications. In this case the object is a big steel disc and the forces to be monitored are those acting on the rim of the disc. This is done with strain gauges that register changes in the internal strain eld. This is then run through a system model that outputs the equivalent force for that strain eld. The system model is created through a static system identication consisting of a series of test pushes on the rim of the disc. This method of system identication has a series of problem mainly that it is time con
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Sun, Luotao. "A fast radio transmitter identification system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0003/MQ41785.pdf.

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40

Carnduff, S. D. "System identification of unmanned aerial vehicles." Thesis, Cranfield University, 2008. http://dspace.lib.cranfield.ac.uk/handle/1826/7583.

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The aim of this research is to examine aspects of system identification for unmanned aerial vehicles (UAVs). The process for aircraft in general can be broken down into a number of steps, including manoeuvre design, instrumentation requirements, parameter estimation, model structure determination and data compatibility analysis. Each of these steps is reviewed and potential issues that could be encountered when analysing UAV data are identified. Problems which may be of concern include lack of space within the airframe to mount sensors and a greater susceptibility to the effects of turbulence
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41

Hägg, Per. "Using Structural Information in System Identification." Licentiate thesis, KTH, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-93262.

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Recent advances in small and cheap communication and sensing have opened up for large scale systems with intricate interconnections and interactions. These applications pose new challenges for analysis and control design. To keep up with the increasing demand on performance and efficiency, accurate models of these systems are needed.  Often some prior knowledge of the system, such as system structure, is available.Prior knowledge should whenever possible be used in system identification to improve the model estimate.This thesis addresses the problem of using prior information about the overall
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42

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

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Teare, David Alan. "Modeling and system identification for rotorcraft." Thesis, Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/17088.

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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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McGinnie, B. Paul. "A balanced view of system identification." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319479.

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Gondhalekar, Aditya Chandrashekhar. "Strategies for Non-Linear System Identification." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511825.

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47

Hopkins, Roisin. "System identification for crash victim simulation." Thesis, Loughborough University, 1995. https://dspace.lboro.ac.uk/2134/7053.

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The work presented in this thesis concerns the identification of vehicle occupant models. Mathematical models of the vehicle occupant are used in the preliminary design and development phase of vehicle design. In the design phase, the model is used to guide the decision on restraint system feasibility. In the development phase the model is used to suggest solutions to problems associated with the dummy trajectory or restraint system performance. Current methods used -to determine such models involve independent component testing. The conditions under which the components are tested are often n
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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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49

吳俊青. "System Identification for Discrete Event Systems." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/41143466099841386784.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>89<br>This thesis studies the system identification problem for a class of controlled discrete event dynamic systems (DEDS). The controlled DEDS is treated as a black box in that inputs (as command from the supervisor to plant) and outputs (as response from the plant to the supervisor) relation can only be externally observed instead of being derived from the otherwise known automaton. Our objective of the intended system identification problem is to reconstruct valid automata that will mimic the input and output relation of a DEDS target. 　Three topics of increase
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Chang, Wen-hung, and 張文鴻. "Model Identification for Dynamical System Using MATLAB System Identification Toolbox." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/68346767767352885572.

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碩士<br>正修科技大學<br>電機工程研究所<br>97<br>This thesis uses MATLAB System Identification Toolbox to identify the auto-regressive exongeneous (ARX) model from input/output data of a dynamical system.Two typical method are used to search the parameters in ARX model. One is the least square method (LSM), the other is a combination of LSM and prediction error method (PEM), which performs an iterative numerical search to find an opimal solution by using the results of LSM as an initial guess. Three types of dynamical system are studied, including a linear second-order single-input-single-output (SISO) suspen
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