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The paper presents a methodology for an optimal input design for model discrimination.
In this paper the problem of optimal input design for model identification is studied.
The paper presents a methodology for optimal input design for model discrimination from experimental data.
In contrast to estimation information measures that are typically used in experimental design for surface estimation, test information measures are most useful in experimental design for model selection and classification problems.
Optimal experimental design for model discrimination (OED-MD) is an efficient tool for discriminating among rival models.
In order to discriminate efficiently among rival models, optimal experiment design for model discrimination (OED-MD) has been developed.
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The campaign was specifically designed for model evaluation purposes.
Different sequential experimental designs for model discrimination and parameter estimation can be performed automatically by the experimental set-up.
We show that the capability of orthogonal designs for model discrimination is surprisingly varied.
The capabilities of model-robust supersaturated designs for model discrimination are assessed using a model-discrimination criterion, the subspace angle.
The December 1969 issue of Model Rocketry (circulation 15,000) carried a press release that began: Reliance Engineering in Albuquerque, New Mexico has announced the formation of a subsidiary company for the manufacture of miniaturized electronic and telemetry systems designed for model rockets.
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