[1] 李永超.民用机场能源信息管理系统[D].北京:北京交通大学, 2010:2-4. (LI Y C. Energy information management system of civil airports[D]. Beijing:Beijing Jiaotong University, 2010:2-4.) [2] 韩君.中国能源需求的建模与实证分析[D].兰州:兰州商学院, 2007:10-20. (HAN J. Modeling and empirical analysis of Chinese energy demand[D].Lanzhou:Lanzhou University of Finance and Economics, 2007:10-20.) [3] 胡雪棉, 赵国浩.基于Matlab的BP神经网络煤炭需求预测模型[J].中国管理科学, 2008, 16(专辑):521-525. (HU X M, ZHAO G H. Forecasting model of coal demand based on Matlab BP neural network[J]. Chinese Journal of Management Science, 2008, 16(Special Issue):521-525.) [4] KROMER P, MUSÍLEK P, PELIKÁN E, et al. Support vector regression of multiple predictive models of downward short-wave radiation[C]//IJCNN 2014:Proceedings of the IEEE 2014 International Joint Conference on Neural Networks. Piscataway, NJ:IEEE, 2014:651-657. [5] 陈海燕, 杨冰欣, 徐涛, 等.基于模糊支持向量回归的机场噪声预测[J].南京航空航天大学学报, 2013, 45(5):722-726. (CHEN H Y, YANG B X, XU T, et al. Airport noise prediction based on fuzzy support vector regression[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2013, 45(5):722-726.) [6] LIN C-F, WAN S-D. Fuzzy support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2):464-471. [7] ZHANG R, DUAN X-B, HAO L. Fuzzy support vector regression for function approximation with noises[C]//ICCASM 2010:Proceedings of 2010 International Conference on Computer Application and System Modeling. Piscataway, NJ:IEEE, 2010, 11:14-17. [8] 邓乃扬, 田英杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社, 2004:32-65.(DENG N Y, TIAN Y J. New Methods in Data Mining-Support Vector Machine (SVM)[M].Beijing:Science Press, 2004:32-65.) [9] 李永娜.基于支持向量机的回归预测综述[J].信息通信, 2014(11):32-33. (LI Y N. The regression prediction reviewed based on Support Vector Machine (SVM)[J]. Information & Communications, 2014(11):32-33.) [10] 赵玉刚, 鞠建波, 张经伟.基于LIB-SVM的电子设备故障预测方法研究[J].计算机测量与控制, 2015, 23(6):1888-1891.(ZHAO Y G, JU J B, ZHANG J W. Research on fault prediction methods of electronic device based on LIB-SVM[J].Computer Measurement & Control, 2015, 23(6):1888-1891.) [11] 黄成泉, 王士同, 蒋亦樟, 等.一种基于L2-SVM的多视角核心向量机[J].控制与决策, 2015, 30(8):1356-1364. (HUANG C Q, WANG S T, JIANG Y Z, et al. A multi-view core vector machine based on L2-SVM[J]. Control and Decision, 2015, 30(8):1356-1364.) [12] 卢振兴, 杨志霞, 高新豫.最小二乘双支持向量回归机[J].计算机工程与应用, 2014, 50(23):140-144. (LU Z X, YANG Z X, GAO X Y. Least square twin support vector regression[J]. Computer Engineering and Applications, 2014, 50(23):140-144.) [13] 王枫, 上官安琪, 夏俊丽.基于改进支持向量机的湖北电网特高压规划研究[J]. 机电工程, 2015, 32(8):1141-1145. (WANG F, SHANGGUAN A Q, XIA J L. UHV planning of Hubei grid based on improved SVM[J].Journal of Mechanical & Electrical Engineering, 2015, 32(8):1141-1145.) [14] 耿俊豹, 孙林凯, 陈是学.支持向量机的混合核函数参数优选方法[J].计算机应用, 2013, 33(5):1321-1323. (GENG J B, SUN L K, CHEN S X. Parameters optimization of combined kernel function for support vector machine[J]. Journal of Computer Applications, 2013, 33(5):1321-1323.) [15] BREUNIG M M, HANS-PETER KRIEGEL, NG R T, et al. LOF:identifying density-based local outliers[C]//SIGMOD '00:Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. New York:ACM, 2000:93-104. |