[1]郭建军,张端金,张中华..基于奇异值分解的Box-Jenkins模型参数估计算法[J].郑州大学学报(工学版),2008,29(03):39-42.[doi:10.3969/j.issn.1671-6833.2008.03.010]
　GUO Jianjun,ZHANG Duanjin,ZHANG Zhonghua.Box-Jenkins model parameter estimation algorithm based on singular value decomposition[J].Journal of Zhengzhou University (Engineering Science),2008,29(03):39-42.[doi:10.3969/j.issn.1671-6833.2008.03.010]

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基于奇异值分解的Box-Jenkins模型参数估计算法()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

29卷

期数:

2008年03期

页码:

39-42

栏目:

出版日期:

1900-01-01

文章信息/Info

Title:

Box-Jenkins model parameter estimation algorithm based on singular value decomposition

作者:

郭建军; 张端金; 张中华.

郑州大学,信息工程学院,河南,郑州,450001, 郑州大学,信息工程学院,河南,郑州,450001, 郑州大学,信息工程学院,河南,郑州,450001

Author(s):

GUO Jianjun; ZHANG Duanjin; ZHANG Zhonghua

关键词:

参数估计;  奇异值分解;  递推广义增广最小二乘法;  Box-Jenkins模型

Keywords:

parameter estimation;  singular value decomposition;  Progressive generalization of the least squares method;  Box-Jenkins model

DOI:

10.3969/j.issn.1671-6833.2008.03.010

文献标志码:

A

摘要:

基于奇异值分解和递推广义增广最小二乘原理,提出了Box-Jenkins模型参数估计的一种递推算法.常规的递推广义增广最小二乘算法对舍入误差较为敏感,会导致协方差矩阵失去正定性和对称性,产生病态条件问题,引起数值不稳定现象.为了改善参数估计的性能,利用协方差矩阵的奇异值分解技术,推导出Box-Jenkins模型估计算法.该算法辨识精度高,收敛速度快,数值稳定性好.仿真表明,随着噪信比的增大.新算法仍然具有良好的性能.

Abstract:

Based on the principle of singular value decomposition and incremental generalization of the least squares principle, a recursive algorithm for parameter estimation of Box-Jenkins model is proposed. The conventional generalized augmented least squares algorithm is sensitive to rounding error, which will cause the covariance matrix to lose positive characterization and symmetry, resulting in the problem of pathological conditions and numerical instability. In order to improve the performance of parameter estimation, the Box-Jenkins model estimation algorithm is derived by using the singular value decomposition technique of covariance matrix. The algorithm has high recognition accuracy, fast convergence speed and good numerical stability. The simulation shows that with the increase of the noise-signal ratio. The new algorithm still has good performance.

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