Abstract
Models and model quality are prime concerns for most design issues in control and system analysis. In this contribution we discuss how to build mathematical models that given certain constraints, are of optimum quality for a prespecified application. We then take into account the influence of both bias errors and random errors on the model. It turns out that for a fairly broad class of identification methods in the prediction error family, the optimal choices of design variables can be given in an explicit form.
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© 1987 Springer-Verlag Berlin Heidelberg
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Ljung, L. (1987). System Identification: Design Variables and the Design Objective. In: Curtain, R.F. (eds) Modelling, Robustness and Sensitivity Reduction in Control Systems. NATO ASI Series, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-87516-8_16
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DOI: https://doi.org/10.1007/978-3-642-87516-8_16
Publisher Name: Springer, Berlin, Heidelberg
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