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Time-varying Dynamic Systems Identification

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System Identification

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

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Abstract

Chapter 8 focuses on the recursive parameter estimation of dynamic systems, where, in general, the optimality of the estimation results of the linear regression models of Chap. 7 will no longer hold. Here the interchanging concept of parameter and state will be further worked out, using extended Kalman filtering and observer-based methods. And, again it will be applied to both the linear and nonlinear cases. The theory is illustrated by real-world examples, with most often a biological component in it, as these cases often show a time-varying behavior due to adaptation of the (micro)organisms.

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Notes

  1. 1.

    The data from “De Poe1 en ’t Zwet,” a lake situated in the western part of the Netherlands, for the period 21–30 April 1983, were collected by students of the University of Twente.

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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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Correspondence to Karel J. Keesman .

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Keesman, K.J. (2011). Time-varying Dynamic Systems Identification. In: System Identification. Advanced Textbooks in Control and Signal Processing. Springer, London. https://doi.org/10.1007/978-0-85729-522-4_8

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  • DOI: https://doi.org/10.1007/978-0-85729-522-4_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-521-7

  • Online ISBN: 978-0-85729-522-4

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