Small Sample Modelling Based on Deep and Broad Forest Regression
Theory and Industrial Application
- 1 Edición - 31 de octubre de 2025
- Última edición
- Autores: Wen Yu, Jian Tang, Junfei Qiao
- Idioma: Inglés
Small Sample Modelling Based on Deep and Broad Forest Regression: Theory and Industrial Application delves into tree-structured methods in the industrial sector, encomp… Leer más
Descripción
Descripción
Puntos claves
Puntos claves
- 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
De interès para
De interès para
Índice
Índice
1. Preliminaries
1.1 Deep forest classification
1.2 Broad learning system
1.3 Decision tree for T-S fuzzy regression
2. Deep Forest Regression for Industrial Modeling
2.1 Basic deep forest regression method
2.2 Deep forest regression based on cross-layer fully connection
2.3 Simulation results
2.4 Conclusions
3. Broad Forest Regression for Industrial Modeling
3.1 Static broad forest regression method
3.2 Broad forest regression with increment learning
3.3 Simulation results
3.4 Conclusion
4. Fuzzy Forest Regression for Industrial Modeling
4.1 Fuzzy regression tree method
4.2 Fuzzy forest regression method
4.3 Time complexity analysis
4.4 Simulation results
4.5 Conclusion
PART II Application to Dioxin Emission Modeling
5. Deep Forest Regression Based on Feature Reduction and Feature Enhancement
5.1 Method strategy
5.2 Method implementation
5.3 Simulation results
5.4 Conclusion
6. Simplified Deep Forest Regression with Combined Feature Selection and Residual Error Fitting
6.1 Method strategy
6.2 Method implementation
6.3 Simulation results
6.4 Conclusion
7. Online Fuzzy Broad Forest Regression
7.1 Method strategy
7.2 Method implementation
7.3 Experimental Results
7.4 Conclusion References Appendix
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 31 de octubre de 2025
- Idioma: Inglés
Sobre los autores
Sobre los autores
WY
Wen Yu
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.
JT
Jian Tang
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.
JQ