Embedded direct search of optimal designs for finite noise experiments

Franziska Schulz; Kurt Frischmuth

doi:10.2478/v10174-010-0008-z

Authors

Franziska Schulz Universitat Rostock, Germany Author
Kurt Frischmuth Universitat Rostock, Germany Author

DOI:

https://doi.org/10.2478/v10174-010-0008-z

Keywords:

parameter identification, nonlinear regression, embedding method, direct search algorithm

Abstract

We study experimental designs for the identification of nonlinear model parameters. As optimality criterion we assume minimality of the error in a huge number of identifications run on simulated data, which are generated with known parameters and a given error distribution. The optimal design depends on the nonlinear parameters. We find the optimal solution set by combining a path following strategy and a direct search method.

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