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Modeling, Design, and Simulation of Systems with UncertaintiesVerified Global Optimization for Estimating the Parameters of Nonlinear Models

Modeling, Design, and Simulation of Systems with Uncertainties: Verified Global Optimization for... [Nonlinear parameter estimation is usually achieved via the minimization of some possibly non-convex cost function. Interval analysis allows one to derive algorithms for the guaranteed characterization of the set of all global minimizers of such a cost function when an explicit expression for the output of the model is available or when this output is obtained via the numerical solution of a set of ordinary differential equations. However, cost functions involved in parameter estimation are usually challenging for interval techniques, if only because of multi-occurrences of the parameters in the formal expression of the cost. This paper addresses parameter estimation via the verified global optimization of quadratic cost functions. It introduces tools for the minimization of generic cost functions. When an explicit expression of the output of the parametric model is available, significant improvements may be obtained by a new box exclusion test and by careful manipulations of the quadratic cost function. When the model is described by ODEs, some of the techniques available in the previous case may still be employed, provided that sensitivity functions of the model output with respect to the parameters are available.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Modeling, Design, and Simulation of Systems with UncertaintiesVerified Global Optimization for Estimating the Parameters of Nonlinear Models

Part of the Mathematical Engineering Book Series (volume 3)
Editors: Rauh, Andreas; Auer, Ekaterina
Kieffer, Michel; Markót, Mihály Csaba; Schichl, Hermann; Walter, Eric
Modeling, Design, and Simulation of Systems with Uncertainties — May 13, 2011
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References (56)

Publisher
Springer Berlin Heidelberg
Copyright
© Springer-Verlag Berlin Heidelberg 2011
ISBN
978-3-642-15955-8
Pages
129 –151
DOI
10.1007/978-3-642-15956-5_7
Publisher site
See Chapter on Publisher Site

Abstract

[Nonlinear parameter estimation is usually achieved via the minimization of some possibly non-convex cost function. Interval analysis allows one to derive algorithms for the guaranteed characterization of the set of all global minimizers of such a cost function when an explicit expression for the output of the model is available or when this output is obtained via the numerical solution of a set of ordinary differential equations. However, cost functions involved in parameter estimation are usually challenging for interval techniques, if only because of multi-occurrences of the parameters in the formal expression of the cost. This paper addresses parameter estimation via the verified global optimization of quadratic cost functions. It introduces tools for the minimization of generic cost functions. When an explicit expression of the output of the parametric model is available, significant improvements may be obtained by a new box exclusion test and by careful manipulations of the quadratic cost function. When the model is described by ODEs, some of the techniques available in the previous case may still be employed, provided that sensitivity functions of the model output with respect to the parameters are available.]

Published: May 13, 2011

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