Identification of dynamic networks with rank-reduced process noise

被引：6

作者：

Weerts, Harm H. M. ^{[1
]}

Van den Hof, Paul M. J. ^{[1
]}

Dankers, Arne G. ^{[2
]}

机构：

[1] Eindhoven Univ Technol, Dept Elect Engn, Control Syst Grp, Eindhoven, Netherlands

[2] Univ Calgary, Dept Elect Engn, Calgary, AB, Canada

来源：

IFAC PAPERSONLINE | 2017年 / 50卷 / 01期

基金：

欧洲研究理事会;

关键词：

System identification; dynamic networks; rank-reduced noise; COMPLEX NETWORKS; IDENTIFIABILITY; SYSTEMS; MODELS;

D O I：

10.1016/j.ifacol.2017.08.1319

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 dynamic network identification usually the assumption is made that there is a full rank process noise affecting the network. For large scale networks with many variables this assumption is not realistic as the noise could be generated by a limited number of sources. We extend prediction error identification methods by allowing rank-reduced process noise in the network. The developed method is based on a modification of the typical predictor expression and an appropriate modification of the identification criterion. It is shown that this method leads to consistent estimates, and we provide a method to reduce the variance of the estimates, which is confirmed by simulations. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：10562 / 10567

页数：6

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