Navigation ▼

Show simple item record

dc.contributor.advisor Parlitz, Ulrich Prof. Dr.
dc.contributor.author Schumann-Bischoff, Jan
dc.date.accessioned 2016-10-17T08:17:09Z
dc.date.available 2016-10-17T08:17:09Z
dc.date.issued 2016-10-17
dc.identifier.uri http://hdl.handle.net/11858/00-1735-0000-002B-7C27-D
dc.language.iso eng de
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc 530 de
dc.title Parameterschätzung und Modellevaluation für komplexe Systeme de
dc.type doctoralThesis de
dc.contributor.referee Parlitz, Ulrich Prof. Dr.
dc.date.examination 2016-04-06
dc.subject.gok Physik (PPN621336750) de
dc.description.abstracteng Mathematical models in form of dynamical systems play an important role in many disciplines, such as system biology, engineering, or physics. They are widely used to simulate the time evolution of the internal states of systems like cardiac muscle cells, neurons, electrical circuits, or the weather, to give just a few examples. Often the models are nonlinear. To obtain realistic results the parameterization of the model equations must be adjusted beforehand so that the model describes the real world system one aims to simulate as accurate as possible. Another task where dynamical systems play an important role is when full states of a real world system are required but only the time evolution of some state variables are actually measured in form of time series. This is a common situation in climatology. There, the temperature, barometric pressure, humidity, etc. are measured at a much smaller number of locations than it would be necessary to obtain the full state of the atmosphere. Nevertheless, the latter is required for example to initialize a weather forecast. In both situations a state and parameter estimation algorithm can be used to estimate full states of the system and model parameters given some measured data time series and a mathematical model of the system. If states and parameters are estimated from data, then one is typically also interested in the uncertainty of the estimated values. Whether states and parameters can be uniquely estimated from the data can by analyzed by investigating the observability of the mathematical model. In this thesis methods for state and parameter estimation as well as for analyzing the uncertainty and observability are presented and discussed. The first part is about the implementation of state and parameter estimation algorithms which are based on solving high dimensional optimization problems. To solve these optimization problems many optimization methods rely on accurate information about derivatives. It is discussed that a technique called automatic differentiation has the capability to provide numerically exact values for derivatives by requiring only a minimum of additional runtime of the estimation method. Using this technique an error prone implementation of derivatives in a computer program is not necessary. In the following parts a method is presented and evaluated which provides a measure for the uncertainty in state and parameter estimation. Among other things results based on this method are compared to results based on an optimization based state and parameter estimation algorithm. It is also investigated how the initialization of the estimation algorithm and the number and combination of measured model variables affect the accuracy of the estimated states and parameters. Furthermore, a method is presented which can be used to identify redundant model parameters and state variables which can not be uniquely estimated. This method also provides information about which and how many parameters may be removed from the estimation task so that all remaining state variables and parameters can be uniquely estimated. Finally, a nonlinear electronic circuit which may exhibit chaotic behavior is realized and used as an experimental system. Based on measured data from this circuit the previously presented methods are evaluated and verified. de
dc.contributor.coReferee Wörgötter, Florentin Prof. Dr.
dc.subject.eng State and Parameter Estimation de
dc.subject.eng Observability de
dc.subject.eng Uncertainty Analysis de
dc.subject.eng Automatic Differentiation de
dc.subject.eng Data Assimilation de
dc.subject.eng Nonlinear Modelling de
dc.identifier.urn urn:nbn:de:gbv:7-11858/00-1735-0000-002B-7C27-D-3
dc.affiliation.institute Fakultät für Physik de
dc.identifier.ppn 870287338

Files in this item

This item appears in the following Collection(s)

Show simple item record