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Data-Driven Identification of Networks of Dynamic Systems

Data-Driven Identification of Networks of Dynamic Systems

Data-Driven Identification of Networks of Dynamic Systems Hardback - 2022

by Michel Verhaegen

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Hardcover. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; An excellent introduction to the identification of network-connected systems, covering models and methods in detail, demonstrating how these methods are applied in adaptive optics, and addressing open questions. Researchers and graduate
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Details

  • Title Data-Driven Identification of Networks of Dynamic Systems
  • Author Michel Verhaegen
  • Binding Hardback
  • Condition New
  • Pages 286
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2022-05-12
  • Features Bibliography, Index
  • Bookseller's Inventory # ria9781316515709_inp
  • ISBN 9781316515709 / 1316515702
  • Weight 1.44 lbs (0.65 kg)
  • Dimensions 9.61 x 6.69 x 0.69 in (24.41 x 16.99 x 1.75 cm)
  • Category Technology & Industrial Arts
  • Library of Congress subjects System analysis - Mathematics, TECHNOLOGY & ENGINEERING / General
  • Library of Congress Catalogue Number 2021056490
  • Dewey Decimal Code 003.85
  • Quantity available 755

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From the publisher

This comprehensive text provides an excellent introduction to the state of the art in the identification of network-connected systems. It covers models and methods in detail, includes a case study showing how many of these methods are applied in adaptive optics and addresses open research questions. Specific models covered include generic modelling for MIMO LTI systems, signal flow models of dynamic networks and models of networks of local LTI systems. A variety of different identification methods are discussed, including identification of signal flow dynamics networks, subspace-like identification of multi-dimensional systems and subspace identification of local systems in an NDS. Researchers working in system identification and/or networked systems will appreciate the comprehensive overview provided, and the emphasis on algorithm design will interest those wishing to test the theory on real-life applications. This is the ideal text for researchers and graduate students interested in system identification for networked systems.

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