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Sinopsis
This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.
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Acerca del autor
Jun Zhao is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology, China.
Chunyang Sheng is currently a lecturer with the School of Electrical Engineering and Automation, Shandong University of Science and Technology, China.
Wei Wang is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology, China.
De la contraportada
This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.
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- EditorialSpringer
- Año de publicación2018
- ISBN 10 3319940503
- ISBN 13 9783319940502
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de edición1
- Número de páginas460
- Contacto del fabricanteno disponible
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Data-Driven Prediction for Industrial Processes and Their Applications
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Springer International Publishing, 2018
ISBN 10: 3319940503
ISBN 13: 9783319940502
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Features data-driven modeling algorithms for different industrial prediction requirementsDiscusses multi-scale (short, median, long) prediction, multi-type prediction (time series and factor-based), and interval-based prediction. Nº de ref. del artículo: 226328079
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Data-Driven Prediction for Industrial Processes and Their Applications (Information Fusion and Data Science)
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Data-Driven Prediction for Industrial Processes and Their Applications
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Springer International Publishing Aug 2018, 2018
ISBN 10: 3319940503
ISBN 13: 9783319940502
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities. 460 pp. Englisch. Nº de ref. del artículo: 9783319940502
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Data-Driven Prediction for Industrial Processes and Their Applications
Publicado por
Springer International Publishing, 2018
ISBN 10: 3319940503
ISBN 13: 9783319940502
Nuevo
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals withinthe machine learning and data analysis and mining communities. Nº de ref. del artículo: 9783319940502
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Data-driven Prediction for Industrial Processes and Their Applications
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Data-driven Prediction for Industrial Processes and Their Applications
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Data-Driven Prediction for Industrial Processes and Their Applications
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Springer International Publishing, Springer International Publishing Aug 2018, 2018
ISBN 10: 3319940503
ISBN 13: 9783319940502
Nuevo
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Neuware -This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals withinthe machine learning and data analysis and mining communities.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 460 pp. Englisch. Nº de ref. del artículo: 9783319940502
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Data-driven Prediction for Industrial Processes and Their Applications
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Data-Driven Prediction for Industrial Processes and Their Applications (Information Fusion and Data Science)
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