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基于小波消噪的混沌多元回归日径流预测模型
引用本文:王秀杰,练继建,费守明,张卓.基于小波消噪的混沌多元回归日径流预测模型[J].系统仿真学报,2007,19(15):3605-3608.
作者姓名:王秀杰  练继建  费守明  张卓
作者单位:1. 天津大学建工学院,天津,300072
2. 天津市水利基建管理处,天津,300204
摘    要:基于小波消噪理论对水文序列中的噪声进行了处理,然后利用混沌理论中的相空间重构技术计算出饱和嵌入维数作为多元回归模型的可控变量个数,将小波、混沌和多元回归方法结合起来对日径流进行了预测。与消噪前相比,消噪处理后建立的模型预测精度有了明显提高:预测合格率提高8%,平均绝对百分比误差为9.53%。因此在对水文时间序列进行混沌分析和预测之前,对其进行小波消噪是完全必要的。

关 键 词:多元回归模型  日径流预测  小波消噪  混沌  嵌入维数
文章编号:1004-731X(2007)15-3605-04
收稿时间:2006-10-27
修稿时间:2006-10-272007-04-05

Chaotic Multivariate Autoregressive Model of Daily Runoff Prediction Based on Wavelet De-noising
WANG Xiu-jie,LIAN Ji-jian,FEI Shou-ming,ZHANG Zhuo.Chaotic Multivariate Autoregressive Model of Daily Runoff Prediction Based on Wavelet De-noising[J].Journal of System Simulation,2007,19(15):3605-3608.
Authors:WANG Xiu-jie  LIAN Ji-jian  FEI Shou-ming  ZHANG Zhuo
Institution:1.School of Civil Engineering, Tianjin University, Tianjin 300072, China; 2.Tianjin Water Capital Construction Department, Tianjin 300204, China
Abstract:The saturated embedding dimension as the member of the controlled variable of the multivariate autoregressive model was computed by the daily runoff time series which was de-noised by wavelet technology. The daily runoff was predicted with the built above model. Compared with the model gained by the original daily runoff time series, the prediction precision is increased obviously. The eligibility rate increases 8% and the mean absolute percentage error is 9.53%. So it is essential that the hydrological time series are de-noised by wavelet method before chaos identification and prediction of the hydrologic system.
Keywords:multivariate autoregressive model  daily rtmoff prediction  wave de-noising  chaos  minimum embedding dimension
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