Small Sample Modelling Based on Deep and Broad Forest Regression

Theory and Industrial Application
Erscheinungsjahr 2025
ISBN: 978-0-443-31564-0
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 570 g

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.

Autoren/Hrsg.

Yu, Wen

Tang, Jian

Qiao, Junfei

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

PART I Methods
1. Preliminaries
2. Deep Forest Regression for Industrial Modeling
3. Broad Forest Regression for Industrial Modeling
4. Fuzzy Forest Regression for Industrial Modeling

PART II Application to Dioxin Emission Modeling
5. Deep Forest Regression Based on Feature Reduction and Feature Enhancement
6. Simplified Deep Forest Regression with Combined Feature Selection and Residual Error Fitting
7. Online Fuzzy Broad Forest Regression

Über Autor(innen)

Yu, Wen
Wen Yu (Fellow, IEEE) received the Ph.D. degrees in electrical engineering from Northeastern University, Shenyang, China. In past he served at Department of Automatic Control, Northeastern University, National Polytechnic Institute (CINVESTAV-IPN), Mexico City, Mexico, Instituto Mexicano del Petroleo, Queen’s University Belfast, U.K., and the University of California at Santa Cruz. He also holds a visiting professorship with Northeastern University, China. Dr. Yu is a member of the Mexican Academy of Sciences.

Qiao, Junfei
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.

Tang, Jian
Jian Tang received the Ph.D. degree from North-eastern University, Shenyang, China, in 2012. He is currently a Professor at Beijing University of Technology and Deputy Director of Beijing Laboratory of Smart Environmental Protection. His current research interests include machine learning based on small sample data, intelligent modelling and control of complex industrial processes, digital twin system of municipal solid waste incineration process.