Data-Driven Identification of Networks of Dynamic Systems (Hardcover, New edition)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	May 2022
Authors:	Michel Verhaegen • Chengpu Yu • Baptiste Sinquin
Dimensions:	250 x 175 x 19mm (L x W x T)
Format:	Hardcover
Pages:	320
Edition:	New edition
ISBN-13:	978-1-316-51570-9
Categories:	Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	1-316-51570-2
Barcode:	9781316515709

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