Zhi-Gen Shang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Zhi-Gen Shang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Hong-Sen Yan
School of Automation Southeast University Nanjing China
2 shared publications Jianqiang He
Department of Automation, Yancheng Institute of Technology, Yancheng 224051, China
1 shared publication- Tong Deng
Wolfson Centre for Bulk Solids Handling Technology, Faculty of Engineering & Science, University of Greenwich, Central Avenue, Chatham ME4 4TB, UK
1 shared publication Xiaohui Duan
Department of Automation, Yancheng Institute of Technology, Yancheng 224051, China
1 shared publication
Latest Publications
- 1 February 2019
- Published by Elsevier in Science of The Total Environment
- Vol. 651, 3043-3052
A novel model for hourly PM2.5 concentration prediction based on CART and EELM
- 1 February 2019
- Published by Elsevier in Science of The Total Environment
- Vol. 651, 3043-3052
A novel model for hourly PM2.5 concentration prediction based on CART and EELM
- Open Access
Product Design Time Forecasting by Kernel-Based Regression with Gaussian Distribution Weights
There exist problems of small samples and heteroscedastic noise in design time forecasts. To solve them, a kernel-based regression with Gaussian distribution weights (GDW-KR) is proposed here. GDW-KR maintains a Gaussian distribution over weight vectors for the regression. It is applied to seek the least informative distribution from those that keep the target value within the confidence interval of the forecast value. GDW-KR inherits the benefits of Gaussian margin machines. By assuming a Gaussian distribution over weight vectors, it could simultaneously offer a point forecast and its confidence interval, thus providing more information about product design time. Our experiments with real examples verify the effectiveness and flexibility of GDW-KR.
Open AccessProduct Design Time Forecasting by Kernel-Based Regression with Gaussian Distribution Weights
There exist problems of small samples and heteroscedastic noise in design time forecasts. To solve them, a kernel-based regression with Gaussian distribution weights (GDW-KR) is proposed here. GDW-KR maintains a Gaussian distribution over weight vectors for the regression. It is applied to seek the least informative distribution from those that keep the target value within the confidence interval of the forecast value. GDW-KR inherits the benefits of Gaussian margin machines. By assuming a Gaussian distribution over weight vectors, it could simultaneously offer a point forecast and its confidence interval, thus providing more information about product design time. Our experiments with real examples verify the effectiveness and flexibility of GDW-KR.
- Open Access
- 16 March 2015
- Published by Taylor & Francis in Applied Artificial Intelligence
- Vol. 29 (3) , 297-312
Method for Product Design Time Forecasting Based on Support Vector Regression with Probabilistic Constraints
- 16 March 2015
- Published by Taylor & Francis in Applied Artificial Intelligence
- Vol. 29 (3) , 297-312
Open AccessMethod for Product Design Time Forecasting Based on Support Vector Regression with Probabilistic Constraints