System Identification - General Aspects and Structure

  • Manfred Deistler


System identification is concerned with finding a good model from, in general, noisy data, i.e. with data driven modeling. Often the task of identification is so complex that it cannot be performed in a naive way with the naked eye. In addition many identification problems share common features. For these reasons methods and theories have been developed which make system identification a subject of its own. This is the case despite the fact that problems of system identification are treated in different and partly rather separated communities, such as in system and control theory, signal processing, statistics and econometrics; the latter is explained by the fact that system identification is a central issue in many branches of science and has a wide range of areas of applications from control of chemical processes to the analysis of earth quake data or forecasting of sales for firms. Accordingly, we will use the term system identification for data driven modeling in general, i.e. not necessarily relating to system and control theory


System Identification Hankel Matrix State Space System ARMA System Subspace Identification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag London Limited 2001

Authors and Affiliations

  • Manfred Deistler
    • 1
  1. 1.Institut für ÖkonometrieOperations Research und Systemtheorie Technische Universität WienWienAustralia

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