STUDY ON MULTISCALE LEAST SQUARES AND APPLICATION IN PARAMETER IDENTIFICATION

摘要：

In various theoretical research and engineering, the long memory processes are widely found and studied in many applications. Maximum likelihood Estimation is usually used to capture the relevant parameters, but it cant be utilized broadly because of the huge burden of computability. In this paper, we firstly use discrete wavelet transform to analyze stochastic processes of time series scale-by-scale, then study the variance and statistical properties of the series over a range of different scales. Subsequently, apply Least Squares Estimation to parameter estimation according to the property that log variance is approximately simple linear equation of log scale, finally a new method named multiscale Least Squares Estimation is put forward. The new algorithm can effectively decrease computation complexity and obtain satisfying estimation precision. This advantage is validated by the data analysis and computer simulation.

关键词： Long Memory Process Multiscale analysis Multiscale Least Squares Estimation Computation Complexity

作者: CHENG-LIN WEN GUANG-JIANG WANG SONG-WEI WANG ZHU-LIN ZHENG

作者单位: Henan Institute Of Science and Technology, Xinxiang 453003,China School of Computer and Information Engineering, Henan University, Kaifeng475001, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 1625-1629

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）