Wen Yu received the B.S. degree from Tsinghua University, Beijing, China in 1990 and the M.S. and Ph.D. degrees, both in Electrical Engineering, from Northeastern University, Shenyang, China, in 1992 and 1995, respectively. Since 1996, he has been with the National Polytechnic Institute (CINVESTAV-IPN), Mexico City, Mexico, where he is currently a professor and department chair of the Automatic Control Department. From 2002 to 2003, he held research positions with the Mexican Institute of Petroleum. He was a Senior Visiting Research Fellow with Queen’s University Belfast, Belfast, U.K., from 2006 to 2007, and a Visiting Associate Professor with the University of California, Santa Cruz, from 2009 to 2010. He gas published more than 100 research papers in reputed journals. His Google Scholar h-index is 33, the citation number is 4100. He serves as associate editors of IEEE Transactions on Cybernetics, Neurocomputing, and Journal of Intelligent and Fuzzy Systems. He is a member of the Mexican Academy of Sciences. 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.

Select Guide Rating

• PART I Methods1. Preliminaries2. Deep Forest Regression for Industrial Modeling3. Broad Forest Regression for Industrial Modeling4. Fuzzy Forest Regression for Industrial ModelingPART II Application to Dioxin Emission Modeling5. Deep Forest Regression Based on Feature Reduction and Feature Enhancement6. Simplified Deep Forest Regression with Combined Feature Selection and Residual Error Fitting7. Online Fuzzy Broad Forest Regression