Model quality: The roles of prior knowledge and data information
1992 (English)In: Proceedings of the IEEE Conference on Decision and Control, Brighton, Engl, 1992, no Piscataway, NJ, United States, 273-278 p.Conference paper (Refereed)
The authors discuss the basic issues involved in the problem of estimating a model's reliability. In particular, the role of prior information is scrutinized. The modeling errors can be divided into two categories, namely, systematic/bias errors and variability/random errors. All serious system identification experiments contain a model validation step. For an unfalsified model, the bias error has not been found to be significantly larger than the random errors. Hence, the traditional, statistical way to provide estimated standard deviations for the model is relevant for unfalsified models. In case of many unfalsified models, a sound scientific approach is to choose the most powerful unfalsified one. The definition of most powerful depends on the intended application. For example, in robust control design an unfalsified model can be said to be most powerful if the Hâˆž error bound is minimized. How this concept relates to minimizing the mean square errors is discussed. A number of questions for further research are identified.
Place, publisher, year, edition, pages
Brighton, Engl, 1992. no Piscataway, NJ, United States, 273-278 p.
, Proceedings of the 30th IEEE Conference on Decision and Control Part 1 (of 3)
Error analysis, Integral equations, Random processes, Statistical methods, Data information, Model quality, Prior knowledge, Identification (control systems)
IdentifiersURN: urn:nbn:se:kth:diva-55431OAI: oai:DiVA.org:kth-55431DiVA: diva2:471593
Sponsors: IEEE Control Systems Soc NR 201408052012-01-022012-01-022013-09-05Bibliographically approved