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- W4214904893 abstract "For an estimation procedure that yields (at least asymptotically) unbiased estimates with minimum variances, the uncertainty concerning the parameter estimates remaining after the experiment can be estimated a priori by the Rao-Cramer lower bound, i.e. by the inverse of the information matrix. Here system identification is done by adjusting the parameters of a computational model using WLS in the frequency domain, and assuming normally distributed measurement errors. With the results of analytical investigations concerning the resulting information matrix when the D-optimality criterion is applied, and by means of computational simulation, it is shown which kind of optimum experimental design is possible under the limiting conditions of experimental practice, and which limits are thereby set to parameter identifiability; the question of a suitable model (and parameter) reduction also arises." @default.
- W4214904893 created "2022-03-05" @default.
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- W4214904893 date "2022-03-02" @default.
- W4214904893 modified "2023-09-26" @default.
- W4214904893 title "On the optimum experimental design for the parametric identification of linear elastomechanical systems" @default.
- W4214904893 doi "https://doi.org/10.1201/9780203738085-50" @default.
- W4214904893 hasPublicationYear "2022" @default.
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