Data-Driven Prediction for Industrial Processes and Their Applications

• Main Authors: Zhao, Jun (Author, http://id.loc.gov/vocabulary/relators/aut), Wang, Wei (http://id.loc.gov/vocabulary/relators/aut), Sheng, Chunyang (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: Information Fusion and Data Science,
• Subjects: Data mining.; Manufactures.; Artificial intelligence.; Quality control.; Reliability.; Industrial safety.; Operations research.; Decision making.; Data Mining and Knowledge Discovery.; Manufacturing, Machines, Tools, Processes.; Artificial Intelligence.; Quality Control, Reliability, Safety and Risk.; Operations Research/Decision Theory.

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...