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Learning-Based Soft Sensing and Predictions for Process Industries: Theory, Methodology and Applications - Softcover
Synopsis
Learning-Based Soft Sensing and Predictions for Process Industries: Theory, Methodology and Applications covers prediction and soft sensing in industrial processes that are subject to specific challenges with AI-empowered learning algorithms. With the aid of a data-driven modeling strategy, the book explores the problems of industrial prediction and soft sensing and formulates a series of learning-based theory, methodologies, and applications. The book introduces the basics of prediction and soft sensing backgrounds, including different categories of prediction theory. Secondly, covers the foundations of machine learning methodologies, including supervised learning prediction, semi-supervised, and self-supervised prediction. Finally, the book examines novel learning-based models/architectures.
- Covers the benefits and an explanation of recent developments in prediction and soft sensing systems
- Unifies existing and emerging concepts surrounding advanced prediction models/architectures
- Provides a series of the latest results in, including, but not limited to, supervised learning, semi-supervised learning, self-supervised learning, probabilistic learning
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About the Authors
Dr. Karimi received the B.Sc. (First Hons.) degree in power systems from the Sharif University of Technology, Tehran, Iran, in 1998, and the M.Sc. and Ph.D. (First Hons.) degrees in control systems engineering from the University of Tehran, Tehran, in 2001 and 2005, respectively. His research interests are in the areas of control systems/theory, mechatronics, networked control systems, intelligent control systems, signal processing, vibration control, ground vehicles, structural control, wind turbine control and cutting processes. He is an Editorial Board Member for some international journals and several Technical Committee. Prof. Karimi has been presented a number of national and international awards, including Alexander-von-Humboldt Research Fellowship Award (in Germany), JSPS Research Award (in Japan), DAAD Research Award (in Germany), August-Wilhelm-Scheer Award (in Germany) and been invited as visiting professor at a number of universities in Germany, France, Italy, Poland, Spain, China, Korea, Japan, India.
Dr Yongxiang Lei received the B.Sc. Degree in Automation from the University of South China in 2017 and an M.Sc. in control engineering from Central South University, Changsha, China in 2020. In 2024, received his Ph.D. degree in Mechanical Engineering from Politecnico di Milano. Dr Lei’s research interests are in the areas of machine learning, prediction & control, industrial process modeling, simulation and application, soft sensing.
From the Back Cover
Learning-Based Soft Sensing and Predictions for Process Industries covers prediction and soft sensing in industrial processes subject to specific challenges with AI-empowered learning algorithms. With the aid of a data-driven modeling strategy, the book explores the problems of industrial prediction and soft sensing, and formulates a series of learning-based theory, methodology and applications. The book introduces the basics of the prediction and soft sensing backgrounds, including the different categories of prediction theory. Secondly, the book looks at the foundations of machine learning methodologies, which covers supervised learning prediction, semi-supervised and self-supervised prediction. Finally, the book examines some novel learning-based models/architectures
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Learning-Based Soft Sensing and Predictions for Process Industries : Theory, Methodology and Applications
Published by
Elsevier Science Okt 2026, 2026
ISBN 10: 0443367590
ISBN 13: 9780443367595
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware - Learning-Based Soft Sensing and Predictions for Process Industries: Theory, Methodology and Applications covers prediction and soft sensing in industrial processes that are subject to specific challenges with AI-empowered learning algorithms. With the aid of a data-driven modeling strategy, the book explores the problems of industrial prediction and soft sensing and formulates a series of learning-based theory, methodologies, and applications. The book introduces the basics of prediction and soft sensing backgrounds, including different categories of prediction theory. Secondly, covers the foundations of machine learning methodologies, including supervised learning prediction, semi-supervised, and self-supervised prediction. Finally, the book examines novel learning-based models/architectures. Seller Inventory # 9780443367595