Data-Driven Prediction for Industrial Processes and Their Applications (Hardcover)

Jun Zhao

ISBN 10: 3319940503 / ISBN 13: 9783319940502

Editore: Springer International Publishing AG, Cham, 2018

Riguardo questo articolo

Hardcover. 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. This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9783319940502

Dati bibliografici

• Titolo: Data-Driven Prediction for Industrial Processes and Their Applications (Hardcover)
• Editore: Springer International Publishing AG, Cham
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 3319940503
• ISBN 13: 9783319940502
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.