Open Access
Issue |
E3S Web Conf.
Volume 218, 2020
2020 International Symposium on Energy, Environmental Science and Engineering (ISEESE 2020)
|
|
---|---|---|
Article Number | 04023 | |
Number of page(s) | 5 | |
Section | Environmental Climate Change Monitoring and Urban Protection Planning | |
DOI | https://doi.org/10.1051/e3sconf/202021804023 | |
Published online | 11 December 2020 |
- Tadros Mty. Uses of sunshine duration to estimate the global solar radiation over eight meteorological stations in Egypt. Renewable Energy 2000, 21(2):231-46. [CrossRef] [Google Scholar]
- Yang Zhao, Yu Wenhong, Zhang Furen. Heat gain analysis of building solar radiation in winter [J]. Journal of solar energy, 2005, 26 (1): 108-l13 [Google Scholar]
- Almorox J, Hontoria C. Global solar radiation estimation using sunshine duration in Spain. Energy Conversion and Management 2004, 45 (9–10):152935. [CrossRef] [Google Scholar]
- Fengguohe. Performance comparison of four classification methods [J]. Computer engineering and application, 2011, 47 (8): 25-26 [Google Scholar]
- Wu Xinling. Classification prediction based on Bayesian method [J]. Computer engineering and application, 2004, 33:195-197 [Google Scholar]
- Yao Li Xiao, Yao Jinxiong, Li Baoqing, Wan Shixin. Short term power load forecasting based on neural network competition classification [J]. Taiyuan electric technology. 2008, 28 (10): 45-48 [Google Scholar]
- Zhang Li, Jiang Hao, Pu An Jian. Neural network classification prediction based on generalized radial basis function [J]. Computer technology and development, 2009, 41 (2): 105-109 [Google Scholar]
- Vapnik V. The nature of statistical learning theory[M]. New York. Springer: 1995. [CrossRef] [Google Scholar]
- Vapnik V. Statistical learning theory[M]. New York. Weily: 1995. [Google Scholar]
- Zhu Ming. Data mining [M]. China University of science and Technology Press, 2002 [Google Scholar]
- Weston J, Watkins C. Multiclass vector machines [c] / M. Verleysen: Proceedings of ESANN99, Brussels, 1999: 41-83. [Google Scholar]
- Xu Qihua, Shi Jun. aeroengine fault diagnosis based on support vector machine [J]. Journal of Aeronautical power, 2005, 20 (2): 298-302 [Google Scholar]
- Hsu Chih-Wei, Lin Chih-Jen. A Comparison on methods for multi-class support vector machines [J]. IEEE Transaction Neural Networks, 2002, 13(2): 415-425. [CrossRef] [Google Scholar]
- Hsu Chih-Wei, Lin Chih-Jen. A Comparison on methods formulti-class support vector machines [J]. IEEE Transaction Neural Networks, 2002, 13(2): 415-425. [CrossRef] [Google Scholar]
- Zheng Yongtao, Liu Yushu. Research on solving multi classification problems with support vector machine [J]. Computer engineering and application, 2005, 23: 190-192. [Google Scholar]
- Chen Yongyi support vector machine method and fuzzy system [J]. Fuzzy system and mathematics, 2005, 19(1): 1-11. [Google Scholar]
- http://www.nrel.gov/midc/srrl_bms/ [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.