Da: Majestic Books, Hounslow, Regno Unito
EUR 138,88
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New. pp. 262.
Condizione: New. pp. 262.
Condizione: New.
Lingua: Inglese
Editore: Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128193654 ISBN 13: 9780128193655
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 151,12
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 155,31
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New. pp. 262.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 170,36
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 165,50
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 187,18
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: preigu, Osnabrück, Germania
EUR 122,10
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches | Theory and Practical Applications | Fouzi Harrou (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Elsevier Inc | EAN 9780128193655 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 188,19
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: moluna, Greven, Germania
EUR 174,11
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven bas.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 118,91
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Elsevier Science & Technology, Elsevier, 2020
ISBN 10: 0128193654 ISBN 13: 9780128193655
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 132,00
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches - such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches - to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Englisch.
Da: Revaluation Books, Exeter, Regno Unito
EUR 138,83
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 262 pages. 8.75x5.75x1.00 inches. In Stock. This item is printed on demand.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 132,00
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches - such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches - to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems.