计算机科学 ›› 2017, Vol. 44 ›› Issue (Z6): 119-122.doi: 10.11896/j.issn.1002-137X.2017.6A.026
张淑娟,邓秀勤,刘波
ZHANG Shu-juan, DENG Xiu-qin and LIU Bo
摘要： 针对税收收入预测存在着非线性、不稳定性和多经济因素影响的复杂性,提出用最小二乘支持向量回归机的方法对广东省从化市的税收收入进行预测,并建立数学模型。由于模型中的参数C和σ2直接影响支持向量机的预测效果,因此巧妙地融合了粒子群优化算法的思想,采用粒子群算法对参数进行寻优来确保预测模型的精确性和稳定性。仿真实验结果表明,相对于各参比模型,用粒子群算法对参数进行寻优的最小二乘支持向量回归机的预测精度有了显著提高,从而说明了该模型的有效性和实用性。
| [1] 中国国际税收研究会课题组.当代税源管理理论[M].北京:人民出版社,2004. [2] 赵云河.逐步回归法在税收预测中的应用研究[J].Journal of Yunan Finance and Economics University,2006,2(5):88-89. [3] 张梦瑶,崔晋川.基于时间序列法的国税月度收入预测模型研究[J].系统科学与数学,2008(11):77-84. [4] 张绍秋,胡跃明.基于BP神经网络的税收预测模型[J].华南理工大学学报(自然科学版),2006,34(6):55-58. [5] VAPNIK V.The Nature of Statistical Learning Theory[M].Springer-Verlag,New York,1995. [6] LIU D H,QIAN H,DAI G,et al.An iterative SVM approach to feature selection and classification in high-dimensional datasets[J].Pattern Recognition,2013,46(9):2531-2537. [7] LI Y F,TSANG I W,KWOK J T,et al.Convex and scalable weakly labeled SVMs[J].Journal of Machine Learning Research,2013,4(1):2151-2188. [8] ALHAM N K,LI M Z,LIU Y,et al.A Map Reduce-based distributed SVM ensemble for scalable image classification and annotation[J].Computers&Mathematics with Applications,2013,66(10):1920-1934. [9] SALEM Y B,NASRI S.Automatic recognition of woven fabrics based on texture and using SVM[J].Signal,Image and Video Processing,2010,4(4):429-434. [10] SHIN J,KIM H J,KIM Y.Adaptive support vector regression for UAV flight control[J],Neural Network,2011,24(1):109-120. [11] AUEPANWIRIYAKUL S,SUMONPHAN E,THEERA-UM-PON N,et al.Automatic Nevirapine concentration interpretation system using support vector regression[J].Computer Methods and Programs in Biomedicine,2011,101(3):271-281. [12] LEVIS A A,PAPAGEORGIOU L G.Customer demand forecasting via support vector regression analysis[J].Chemical Engineering Research and Design,2003(83):1009-1018. [13] GAOA C,BOMPARD E,NAPOLI R,et al.Price forecast in the competitive electricity market by support vector machine[J].Physica A:Statistical Mechanics and its Applications,2007,2(1):98-113. [14] VAN GESTEL T,SUYKENS J A K, BAESTAENS D E,et al.Financial time series prediction using least squares support vector machines within the evidence framework[J].IEEE Transcations on Neural Networks,2001,12(4):809-821. [15] PAI P F,LIN K P,LIN C S,et al.Time series forecasting by a seasonal support vector regression model[J].Expert Systems with Applications,2010,37(6):4261-4265. [16] LI X,HU B,DU R.Predicting the parts weight in plastic injection molding using least squares support vector regression[J].IEEE Transactions on Systems,Man,and Cybernetics-Part C:Applications and Review,2008,38(6):827-833. [17] KUHN H W,TUCKER A W.Nonlinear Programming[C]∥Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability,1951:481-492. [18] MERCER J.Function of positive and negative type and theirconnection with the theory of integral equations[J].Philosophical Transactions of the Royal Society,1909,209:415-446. |
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