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Computation of Parameters in Separable Nonlinear Dynamic Models by use of Short-Cut Methods
Umeå University, Faculty of Science and Technology, Computing Science.
2002 In: Technical Report UMINF, Vol. 02.14Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2002. Vol. 02.14
URN: urn:nbn:se:umu:diva-4625OAI: diva2:143808
Available from: 2005-08-18 Created: 2005-08-18Bibliographically approved
In thesis
1. Computation of Parameters in some Mathematical Models
Open this publication in new window or tab >>Computation of Parameters in some Mathematical Models
2002 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In computational science it is common to describe dynamic systems by mathematical models in forms of differential or integral equations. These models may contain parameters that have to be computed for the model to be complete. For the special type of ordinary differential equations studied in this thesis, the resulting parameter estimation problem is a separable nonlinear least squares problem with equality constraints. This problem can be solved by iteration, but due to complicated computations of derivatives and the existence of several local minima, so called short-cut methods may be an alternative. These methods are based on simplified versions of the original problem. An algorithm, called the modified Kaufman algorithm, is proposed and it takes the separability into account. Moreover, different kinds of discretizations and formulations of the optimization problem are discussed as well as the effect of ill-conditioning.

Computation of parameters often includes as a part solution of linear system of equations Ax = b. The corresponding pseudoinverse solution depends on the properties of the matrix A and vector b. The singular value decomposition of A can then be used to construct error propagation matrices and by use of these it is possible to investigate how changes in the input data affect the solution x. Theoretical error bounds based on condition numbers indicate the worst case but the use of experimental error analysis makes it possible to also have information about the effect of a more limited amount of perturbations and in that sense be more realistic. It is shown how the effect of perturbations can be analyzed by a semi-experimental analysis. The analysis combines the theory of the error propagation matrices with an experimental error analysis based on randomly generated perturbations that takes the structure of A into account

22 p.
inverse problems, parameter estimation, systems of ordinary differential equations, short-cut methods, separable nonlinear least squares problems, Matlab toolbox (for nonlinear least squares), linear systems of equations, pseudoinverse solution, perturbation theory, singular value decomposition, experimental error analysis
National Category
Computer Science
urn:nbn:se:umu:diva-565 (URN)
Public defence
Available from: 2005-08-18 Created: 2005-08-18Bibliographically approved

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