The model equivalence based parameter estimation methods for Box-Jenkins systems

被引：12
作者：
Ding, Feng [1 ]
Meng, Dandan [1 ]
Wang, Qi [1 ]
机构：
[1] Nanchang Hangkong Univ, Sch Informat Engn, Nanchang 330063, Peoples R China
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2015年 / 352卷 / 12期
基金：
中国国家自然科学基金;
关键词：
NONLINEAR-SYSTEMS; ESTIMATION ALGORITHM; ITERATIVE ESTIMATION; STOCHASTIC-SYSTEMS; ERROR SYSTEMS; IDENTIFICATION;
D O I：
10.1016/j.jfranklin.2015.08.018
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a model equivalence based recursive extended least squares algorithm for output-error autoregressive moving average (i.e., Box-Jenkins) systems. The key is to transform a Box Jenkins system into a controlled autoregressive moving average system by the model equivalent transformation, to estimate the parameters of the new system, and to compute the parameter estimates of the original system by comparing coefficients of polynomials. In order to show advantages of the proposed algorithm, this paper gives an auxiliary model based recursive generalized extended least squares (AM-RGELS) algorithm for comparison. The simulation results indicate that the proposed algorithm can improve the parameter estimation accuracy compared with the AM-RGELS algorithm. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：5473 / 5485
页数：13
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