Lingua: Inglese
Editore: Butterworth-Heinemann 2016-07-11, 2016
ISBN 10: 0128031360 ISBN 13: 9780128031360
Da: Chiron Media, Wallingford, Regno Unito
EUR 104,10
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback. Condizione: New.
Da: Revaluation Books, Exeter, Regno Unito
EUR 115,84
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 1st edition. 282 pages. 8.75x6.00x0.50 inches. In Stock.
Lingua: Inglese
Editore: Elsevier - Health Sciences Division, 2016
ISBN 10: 0128031360 ISBN 13: 9780128031360
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 127,11
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 93,30
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 150,00
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. Englisch.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 156,09
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained.