Small Sample Modelling Based on Deep and Broad Forest Regression
Language: English
Published by Elsevier Inc Okt 2025, 2025
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Condition: New
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Add to basketItem description from seller
Neuware - Small Sample Modelling Based on Deep and Broad Forest Regression: Theory and Industrial Application delves into tree-structured methods in the industrial sector, encompassing classical ensemble learning, tree-structured deep forest classification, and broad learning systems with neural networks. It introduces an innovative deep/broad learning algorithm for small-sample industrial modeling tasks. The book is divided into two parts: methodology and practical application in dioxin emission modeling. Methodology sections include Preliminaries, Deep Forest Regression, Broad Forest Regression, and Fuzzy Forest Regression. The application part focuses on modeling dioxin emissions in municipal solid waste incineration. Throughout, various tree-structured strategies are presented, and the authors provide software systems for validating these methods. This book is suitable for advanced undergraduates, graduate engineering students, and practicing engineers looking for self-study resources.…
Seller Inventory # 9780443315640
- Title
- Small Sample Modelling Based on Deep and Broad Forest Regression
- Author
- We Yu
- Publisher
- Elsevier Inc Okt 2025
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0443315647
- ISBN 13
- 9780443315640
- Item weight
- 475 grams
- Dimensions
- 229x152x19 mm
- Introduces a novel deep and broad regression algorithm specifically designed for small sample industrial modeling. It covers Deep Forest Regression for Industrial Modeling, Broad Forest Regression for Industrial Modeling, and Fuzzy Forest Regression for Industrial Modeling
- Delves into recent results concerning the hot topic of deep and broad learning using non-neuron units for regression and the interpretability of fuzzy trees. These innovative methods are supported by the use of multi-dimensional benchmark data, providing solid confirmation
- Offers a real application case for industrial modeling by focusing on dioxin emission concentration. This case revolves around a strict controlled environment index of the municipal solid waste incineration (MSWI) process. The book provides offline modeling techniques such as improved deep forest regression and simplified deep forest regression
"Synopsis" may belong to another edition of this title.
About the Author
Jian Tang received a Ph.D. degree in control theory and control engineering from Northeastern University, China, in 2012. He is currently a Professor with the Faculty of Information Technology, Beijing University of Technology, Beijing, China. His current research interests include machine learning based on small sample data, intelligent modeling and control of complex industrial process, digital twin system of municipal solid waste incineration process.
Junfei Qiao received B.S. and M.S. degrees in control engineering from Liaoning Technical University, China, in 1992 and 1995, respectively, and a Ph.D. degree in control theory and control engineering from Northeastern University, China, in 1998. He is currently a Professor with the Faculty of Information Technology, Beijing University of Technology, China. His current research interests include neural networks, intelligent systems, and modeling and optimal control of complex industrial processes.
"About the title" may belong to another edition of this title.
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