刘春波,王鲜芳,潘丰.基于蚁群优化算法的支持向量机参数选择及仿真[J].中南大学学报:自然科学版,2008,39(6):1309-1313.Liu Chunbo,Wang Xianfang,Pan Feng.Parameters selection and simulation of support vector machines based on ant colony optimization algorithm[J].Journal of Central South University:Natural Science Edition,2008,39(6):1309-1313.
[2]
曾勍炜,徐知海,吴键.基于改进粒子群优化和支持向量机的电力负荷预测[J].微电子学与计算机,2011,28(1):147-149,153.Zeng Qingwei,Xu Zhihai,Wu Jian.Forecasting of electricity load based on particle swarm optimization and support vector machine[J].Microelectronics&Computer,2011,28(1):147-149,153.
[3]
吴景龙,杨淑霞,刘承水.基于混合遗传算法的支持向量机短期负荷预测方法[J].中南大学学报,2008,40(1):180-184.Wu Jinglong,Yang Shuxia,Liu Chengshui.Parameter selection for support vectormachines based on genetic algorithms to short-term power load forecasting[J].Journal of Central South University,2008,40(1):180-184.
[4]
Gestel T V,Suykens J A K,Baesens B,et al.Benchmarking least squares support vector machine classifiers[J].Machine Learning,2004,54(1):5-32.
[5]
Burges C J C.A tutorial on support vector machines for pattern recognition[J].Data Mining and Knowledge Discovery,1998,2(2):121-167.
[6]
刘孝杰,苏小林,阎晓霞,等.面向主动响应和售电市场的主动配电系统负荷预测[J].电力系统及其自动化学报,2017,29(2):121-127.Liu Xiaojie,Su Xiaolin,Yan Xiaoxia,et al.Load forecast of active distribution system based on active response and electricity market[J].Proceedings of the CSU-EPSA,2017,29(2):121-127.
[7]
王建国,张文兴.支持向量机——建模及其智能优化[M].北京:清华大学出版社,2015.Wang Jianguo,Zhang Wenxing.Support Vector Machine-Modeling and Optimization[M].Beijing:Tsinghua University Press,2015.
[8]
Vapnik V,Chervoknenkis A Y.Theory of pattern recognition[M].Moscow:Nauka,1974.
[9]
Burges C.Geometry and invariance in kernel based methods[C]//Advanced in Kernel Methods-Support Vector Learning,Cambridge,MA:MIT Press,1998:640-646.
[10]
Shevade S K,Keerthi S S,Bhattacharyya C,et al.Improvements to the SMO algorithms for SVM regression[J].IEEE Transactions on Neural Networks,2000,11(5):1188-1193.
[11]
李希鹏.基于混合核函数支持向量机的文本分类研究[D].青岛:中国海洋大学,2012.Li Xipeng.Research on text classification based on support vector machine with mixture of kernels[D].Qingdao:Ocean University of China,2012.
[12]
Wang Jianguo,Hang Zhijie,Hang Wenxing.Support vector machine based on double-population particle swarm optimization[J].Journal of Convergence Information Technology,2013,8(8):898-905.
[13]
康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨[J].电力系统自动化,2004,28(17):1-9.Kang Chongqing,Xia Qing,Zhang Boming.Review and prospect of power system load forecasting[J].Automation of Electriec Power Systems,2004,28(17):1-9.
[14]
周志华.机器学习[M].北京:清华大学出版社,2016.Zhou Zhihua.Machine Learning[M].Beijing:Tsinghua University Press,2016.
[15]
彭宇,彭喜元,刘兆庆.微粒群算法参数效能的统计分析[J].电子学报,2004,32(2):209-213.Peng Yu,Peng Xiyuan,Liu Zhaoqing.Statistic analysis on parameter efficiency of particle swarm optimization[J].Acta Electronica Sinica,2004,32(2):209-213.