[1]陆建山,王昌明,张爱军.重构相点L1范数层次组合预测方法[J].南京理工大学学报(自然科学版),2013,37(02):286.
 Lu Jianshan,Wang Changming,Zhang Aijun.Hierarchical combined forecasting based on L1 norm of reconstructed phase points[J].Journal of Nanjing University of Science and Technology,2013,37(02):286.
点击复制

重构相点L1范数层次组合预测方法
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
37卷
期数:
2013年02期
页码:
286
栏目:
出版日期:
2013-04-30

文章信息/Info

Title:
Hierarchical combined forecasting based on L1 norm of reconstructed phase points
作者:
陆建山王昌明张爱军
南京理工大学 机械工程学院,江苏 南京 210094
Author(s):
Lu JianshanWang ChangmingZhang Aijun
School of Mechanical Engineering,NUST,Nanjing 210094,China
关键词:
组合预测 相空间重构 L1范数 层次结构 稳定平台
Keywords:
combined forecasting phase space reconstruction L1 norm hierarchical structure stabilized platform
分类号:
TP301.6
摘要:
为克服时滞现象对系统状态带来的影响,结合相空间重构理论和组合预测方法,提出一种基于重构相点L1范数的层次组合预测新方法。对于混沌系统状态序列,使用延迟坐标状态空间重构技术进行相空间重构后,应用层次结构组合预测方法研究相点L1范数的演变过程,并通过反解相点L1范数预测值得到原时间序列的预测值。组合预测法中采用AR(p)模型和新陈代谢GM(1,1)模型作为单一预测模型,应用层次结构权值确定方法求取组合预测方法中各模型的组合权重。最后为提高预测精度,使用残差GM(1,1)方法对预测值进行补偿。实验数据的仿真对比分析给出了算法性能评价结果,验证了算法的有效性。
Abstract:
To overcome the damage from the system time delay,a new hierarchical combined forecasting method based on L1 norm of reconstructed phase points is proposed using the phase space reconstruction theory and the combined forecasting method.For chaotic system series,the phase space delay coordinate reconstitution theory is applied to reconstruct the phase space,the hierarchical combined forecasting method is used to analyze the change laws of phase point L1 norm,and forecasting values of time series are obtained with the L1 norm inverse solution.The AR(p)model and metabolism GM(1,1)model are employed as single models while the hierarchical structure based the weight determining method is used to ascertain the weights of each single model.To improve the forecasting precision,residual errors are compensated with GM(1,1)model.The algorithm performance evaluation is obtained by simulating the measured data,and the simulating results verify the method effective.

参考文献/References:

[1] Lee Cheng-chung,Wan Terng-jou,Kuo Chao-yin,et al.Estimating air quality in a traffic tunnel using a forecasting combination model[J].Environmental Monitoring and Assessment,2006,112(1-3):327-345.
[2]Sfetsos A,Siriopoulos C.Combinatorial time series forecasting based on clustering algorithms and neural networks[J].Neural Computing and Applications,2004,13(1):56-64.
[3]Jiang Aihua,Mei Chi,E Jiaqiang,et al.Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application[J].Journal of Central South University of Technology,2010,17(4):863-867.
[4]陈华友,刘春林.基于L1范数的加权几何平均组合预测模型的性质[J].东南大学学报(自然科学版),2004,34(4):535-540.
Chen Huayou,Liu Chunlin.Properties of weighted geometric means combination forecasting model based on L1 norm[J].Journal of Southeast University(Natural Science Edition),2004,34(4):535-540.
[5]刘劲松.多步自适应组合预测及其应用[J].佳木斯大学学报(自然科学版),2009,27(4):535-537.
Liu Jinsong.Adaptive combination of multi-step forecast and its application[J].Journal of Jiamusi University(Natural Science Edition),2009,27(4):535-537.
[6]陈淑燕,王炜.交通量的灰色神经网络预测方法[J].东南大学学报(自然科学版),2004,34(4):541-544.
Chen Shuyan,Wang Wei.Grey neural network forecasting for traffic flow[J].Journal of Southeast University(Natural Science Edition),2004,34(4):541-544.
[7]周湶,任海军,李健,等.层次结构下的中长期电力负荷变权组合预测方法[J].中国电机工程学报,2010,30(16):47-52.
Zhou Quan,Ren Haijun,Li Jian,et al.Variable weight combination method for mid-long term power load forecasting based on hierarchical structure[J].Proceedings of the CSEE,2010,30(16):47-52.
[8]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用[M].武汉:武汉大学出版社,2002.
[9]刘伟,王科俊,邵克勇.混沌时间序列的混合粒子群优化预测[J].控制与决策,2007,22(5):562-565.
Liu Wei,Wang Kejun,Shao Keyong.Predicting chaotic time series using hybrid particle swarm optimization algorithm[J].Control and Decision,2007,22(5):562-565.
[10]李夕兵,刘志祥.基于重构相空间充填体变形规律的灰色预测研究[J].安全与环境学报,2004,4(6):54-57.
Li Xibing,Liu Zhixiang.Research on grey prediction of deformation laws in backfill based on phase space reconstruction[J].Journal of Safety and Environment,2004,4(6):54-57.
[11]Maguire L P,Roche B,McGinnity T M,et al.Predicting a chaotic time series using a fuzzy neural network[J].Information Sciences,1998,112(1):125-136.
[12]江田汉,束炯.基于LSSVM的混沌时间序列的多步预测[J].控制与决策,2006,21(1):77-80.
Jiang Tianhan,Shu Jiong.Multi-step prediction of chaotic time series using the least squares support vector machines[J].Control and Decision,2006,21(1):77-80.
[13]Takens F.Determining strange attractors in turbulence[J].Lecture Notes in Mathematics(Berlin:Springer),1981,898:361-381.
[14]张智晟,孙雅明,王兆峰,等.优化相空间近邻点与递归神经网络融合的短期负荷预测[J].中国电机工程学报,2003,23(8):44-49.
Zhang Zhisheng,Sun Yaming,Wang Zhaofeng,et al.A new STLF approach based on the fusion of optimal neighbor points in phase space and the recursive neural network[J].Proceedings of the CSEE,2003,23(8):44-49.
[15]马洁,李国斌.船舶横摇运动的时间序列预报[J].北京机械工业学院学报,2006,21(1):4-7.
Ma Jie,Li Guobin.Time series prediction of ship rolling[J].Journal of Beijing Institute of Machinery,2006,21(1):4-7.
[16]顾晓辉,王晓鸣,赵有守.直升机航迹的灰色预测[J].南京理工大学学报,2001,25(3):242-246.
Gu Xiaohui,Wang Xiaoming,Zhao Youshou.Grey forecast about helicopter tracks[J].Journal of Nanjing University of Science and Technology,2001,25(3):242-246.
[17]邓聚龙.灰预测与灰决策[M].武汉:华中科技大学出版社,2002.

相似文献/References:

[1]刘 蕴,焦 妍,王华东.改进极限学习机的网络流量混沌预测[J].南京理工大学学报(自然科学版),2017,41(04):454.[doi:10.14177/j.cnki.32-1397n.2017.41.04.009]
 Liu Yun,Jiao Yan,Wang Huadong.Chaotic prediction of network traffic based onimproved extreme learning machine[J].Journal of Nanjing University of Science and Technology,2017,41(02):454.[doi:10.14177/j.cnki.32-1397n.2017.41.04.009]

备注/Memo

备注/Memo:
收稿日期:收稿日期:2011-07-14 修回日期:2011-12-10
作者简介:陆建山(1986-),男,博士生,主要研究方向:数据融合、智能控制技术,E-mail:lujianshan@sohu.com;
通讯作者:王昌明(1952-),男,教授,博士生导师,主要研究方向:水下导航定位技术、智能测控技术及系统,E-mail:wangchangming@njust.edu.cn。
更新日期/Last Update: 2013-04-30