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Perspectives on System Identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2010 (English)In: Annual Reviews in Control, ISSN 1367-5788, E-ISSN 1872-9088, Vol. 34, no 1, 1-12 p.Article in journal (Refereed) Published
Abstract [en]

System identification is the art and science of building mathematical models of dynamic systems from observed input-output data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications System identification is a very large topic, with different techniques that depend on the character of the models to be estimated: linear, nonlinear, hybrid, nonparametric, etc At the same time, the area can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. The area has many facets and there are many approaches and methods A tutorial or a survey in a few pages is not quite possible Instead, this presentation aims at giving an overview of the "science" side, i.e. basic principles and results and at pointing to open problem areas in the practical, "art", side of how to approach and solve a real problem.

Place, publisher, year, edition, pages
Elsevier, 2010. Vol. 34, no 1, 1-12 p.
Keyword [en]
System identification, Mathematical models, Estimation, Non-linear models, Statistical methods
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-58386DOI: 10.1016/j.arcontrol.2009.12.001ISI: 000278691000001OAI: diva2:343291
Available from: 2010-08-13 Created: 2010-08-11 Last updated: 2013-09-23

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