Data-Driven Prediction for Industrial Processes and Their Applications (Hardcover, 1st ed. 2018)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Information Fusion and Data Science
Release date:	August 2018
First published:	2018
Authors:	Jun Zhao • Wei Wang • Chunyang Sheng
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	443
Edition:	1st ed. 2018
ISBN-13:	978-3-319-94050-2
Categories:	Books > Business & Economics > Business & management > Management & management techniques > Operational research Books > Professional & Technical > Mechanical engineering & materials > Production engineering > Reliability engineering Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-319-94050-3
Barcode:	9783319940502

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