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Linear Dynamic System Identification

  • Oliver Nelles

Abstract

The term linear system identification often refers exclusively to the identification of linear dynamic systems. In this chapter’s title the term “dynamic” is explicitly mentioned to emphasize the clear distinction from static systems. An understanding of the basic concepts and the terminology of linear dynamic system identification is required in order to study the identification of nonlinear dynamic systems, which is the subject of all subsequent chapters. The purpose of this chapter is to introduce the terminology, concepts, and algorithms for linear system identification. Since this book deals extensively with local linear models as a very promising approach to nonlinear system identification, most of the methods discussed in this chapter can be transferred to this particular class of nonlinear models. It is one of the main motivations for the use of local linear model approaches that many existing and well-understood linear techniques can be successfully extended for nonlinear processes. A more detailed treatment of linear system identification can be found in [40, 81, 171, 172, 193, 233, 360]. Practical experience can be easily gathered by playing around with the MATLAB system identification toolbox [234].

Keywords

Output Feedback Noise Model Finite Impulse Response Recursive Little Square Infinite Impulse Response 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Oliver Nelles
    • 1
  1. 1.UC Berkeley / TU DarmstadtKronbergGermany

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