On maximum likelihood identification of errors-in-variables models

被引：1
|
作者：
Bottegal, Giulio [1 ]
Risuleo, Riccardo S. [2 ]
Zamani, Mohsen [3 ]
Ninness, Brett [3 ]
Hjalmarsson, Hakan [2 ]
机构：
[1] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands
[2] KTH Royal Inst Technol, Sch Elect Engn, ACCESS Linnaeus Ctr, Stockholm, Sweden
[3] Univ Newcastle, Sch Elect Engn & Comp Sci, Callaghan, NSW 2308, Australia
来源：
IFAC PAPERSONLINE | 2017年 / 50卷 / 01期
基金：
欧洲研究理事会; 瑞典研究理事会;
关键词：
FRISCH SCHEME; LINEAR-SYSTEMS; INPUT; IDENTIFIABILITY; ACCURACY;
D O I：
10.1016/j.ifacol.2017.08.634
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we revisit maximum likelihood methods for identification of errors in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2824 / 2829
页数：6
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