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A search grid for parameter optimization as a byproduct of model sensitivity analysis

Author

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  • Verwaeren, Jan
  • Van der Weeën, Pieter
  • De Baets, Bernard

Abstract

Inverse problem solving, i.e. the retrieval of optimal values of model parameters from experimental data, remains a bottleneck for modelers. Therefore, a large variety of (heuristic) optimization algorithms has been developed to deal with the inverse problem. However, in some cases, the use of a grid search may be more appropriate or simply more practical. In this paper an approach is presented to improve the selection of the grid points to be evaluated and which does not depend on the knowledge or availability of the underlying model equations. It is suggested that using the information acquired through a sensitivity analysis can lead to better grid search results. Using the sensitivity analysis information, a Gauss–Newton-like matrix is constructed and the eigenvalues and eigenvectors of this matrix are employed to transform naive search grids into better thought-out ones. After a theoretical analysis of the approach, some computational experiments are performed using a simple linear model, as well as more complex nonlinear models.

Suggested Citation

  • Verwaeren, Jan & Van der Weeën, Pieter & De Baets, Bernard, 2015. "A search grid for parameter optimization as a byproduct of model sensitivity analysis," Applied Mathematics and Computation, Elsevier, vol. 261(C), pages 8-27.
  • Handle: RePEc:eee:apmaco:v:261:y:2015:i:c:p:8-27
    DOI: 10.1016/j.amc.2015.03.064
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    References listed on IDEAS

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    1. Wu, Lin & Le Dimet, François-Xavier & de Reffye, Philippe & Hu, Bao-Gang & Cournède, Paul-Henry & Kang, Meng-Zhen, 2012. "An optimal control methodology for plant growth—Case study of a water supply problem of sunflower," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(5), pages 909-923.
    2. Papáček, Štěpán & Čelikovský, Sergej & Rehák, Branislav & Štys, Dalibor, 2010. "Experimental design for parameter estimation of two time-scale model of photosynthesis and photoinhibition in microalgae," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(6), pages 1302-1309.
    3. Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2011. "A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(9), pages 1901-1909.
    4. Dirk J.W. De Pauw & Peter A. Vanrolleghem, 2006. "Practical aspects of sensitivity function approximation for dynamic models," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 12(5), pages 395-414, October.
    5. Bar Massada, Avi & Carmel, Yohay, 2008. "Incorporating output variance in local sensitivity analysis for stochastic models," Ecological Modelling, Elsevier, vol. 213(3), pages 463-467.
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