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Bibliografische Details

Titel: Data-Driven Prediction for Industrial ...

Verlag: Springer

Erscheinungsdatum: 2018

Einband: PAP

Zustand: New

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Inhaltsangabe:

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.

�ber die Autorin bzw. den 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.

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