Da: Majestic Books, Hounslow, Regno Unito
EUR 185,57
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Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. 1st edition NO-PA16APR2015-KAP.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 209,65
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 227,16
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 229,23
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 249,34
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 251,17
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Da: moluna, Greven, Germania
EUR 243,35
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Aggiungi al carrelloCondizione: New. Shows how to analyze, in great detail, the industrial operational status through spatio-temporal representation learningCovers how to establish robust monitoring models for industrial processes with irregular dataIndicates how to a.
Lingua: Inglese
Editore: Elsevier Science Publishing Co Inc Jul 2025, 2025
ISBN 10: 044333675X ISBN 13: 9780443336751
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 298,54
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregular data. Two classes of robust modeling methods are highlighted, including low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly, the Laplace distribution is explored, which is adopted to measure the process uncertainty to develop robust monitoring models.The book not only discusses the complex models but also their real-world applications in industry.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 165,86
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Da: Revaluation Books, Exeter, Regno Unito
EUR 186,45
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 300 pages. 9.00x6.00x9.02 inches. In Stock. This item is printed on demand.
Lingua: Inglese
Editore: Elsevier Science Publishing Co Inc, San Diego, 2025
ISBN 10: 044333675X ISBN 13: 9780443336751
Da: CitiRetail, Stevenage, Regno Unito
EUR 180,34
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregular data. Two classes of robust modeling methods are highlighted, including low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly, the Laplace distribution is explored, which is adopted to measure the process uncertainty to develop robust monitoring models.The book not only discusses the complex models but also their real-world applications in industry. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.