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Static Systems Identification

  • Chapter
System Identification

Part of the book series: Advanced Textbooks in Control and Signal Processing ((C&SP))

Abstract

In Chap. 5 we start with the identification of static linear systems, that is, no dynamics are involved. The output of a static system depends only on the input at the same instant and thus shows instantaneous responses. In particular, the so-called least-squares method is introduced. As will be seen in Chaps. 5 and 6, the least-squares method for the static linear case forms the basis for solving nonlinear and dynamic estimation problems. For the analysis of the resulting estimates, properties like bias and accuracy are treated. Special attention is paid to errors-in-variables problems, which allow noise in both input and output variables, to maximum likelihood estimation as a unified approach to estimation, in particular well-defined in the case of normal distributions, and to bounded-noise problems for cases with small data sets.

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Authors and Affiliations

  1. Systems and Control Group, Wageningen University, Bornse Weilanden 9, 6708 WG, Wageningen, Netherlands

    Karel J. Keesman

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  1. Karel J. Keesman
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Keesman, K.J. (2011). Static Systems Identification. In: System Identification. Advanced Textbooks in Control and Signal Processing. Springer, London. https://doi.org/10.1007/978-0-85729-522-4_5

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  • DOI: https://doi.org/10.1007/978-0-85729-522-4_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-521-7

  • Online ISBN: 978-0-85729-522-4

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