Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregular data. Two classes of robust modeling methods are highlighted, including low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly, the Laplace distribution is explored, which is adopted to measure the process uncertainty to develop robust monitoring models.
The book not only discusses the complex models but also their real-world applications in industry.Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Chunhui Zhao is a Qiushi distinguished professor at Zhejiang University in China, and an expert in intelligent industrial monitoring with 20 years of experience in this field. She has authored or co-authored more than 400 papers in peer-reviewed international journals and conferences. Her research interests include statistical machine learning and data mining for industrial applications.
Wanke Yu is a research fellow at the School of Electrical & Electronic Engineering, Nanyang Technological University in Singapore. Wanke Yu received his Ph.D. degree in automatic control from Zhejiang University, Hangzhou, China, in 2020. His research interests include probabilistic graphic model, deep neural network, and nonconvex optimization, and their applications to process control.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Paperback. Etat : new. Paperback. Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregular data. Two classes of robust modeling methods are highlighted, including low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly, the Laplace distribution is explored, which is adopted to measure the process uncertainty to develop robust monitoring models.The book not only discusses the complex models but also their real-world applications in industry. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9780443336751
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