Heng Tao Shen; Jinbao Li; Minglu Li; Jun Ni; Wei Wang Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2006) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Jun Wang; Zhang Yi; Jacek M. Zurada; Bao-Liang Lu; Yin Hujun Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2006) Saatavuus: Painos loppu Pehmeäkantinen kirja
Springer Sivumäärä: 443 sivua Asu: Kovakantinen kirja Julkaisuvuosi: 2018, 30.08.2018 (lisätietoa) Kieli: Englanti
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.