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Embedded Direct Search of Optimal Designs for Finite Noise Experiments

DOI: 10.2478/v10174-010-0008-z

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

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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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