Nonparametric identification of Kronecker networks

被引：9
作者：
Zorzi, Mattia [1 ]
机构：
[1] Univ Padua, Dipartimento Ingn Informaz, Via Gradenigo 6-B, I-35131 Padua, Italy
来源：
AUTOMATICA | 2022年 / 145卷
关键词：
Linear system identification; Sparsity inducing priors; Kernel-based methods; Gaussian processes; SYSTEM-IDENTIFICATION; REGULARIZATION; ESTIMATORS; SELECTION; MODELS; CONVEX; ERROR;
D O I：
10.1016/j.automatica.2022.110518
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We address the problem to estimate a dynamic network whose edges describe Granger causality relations and whose topology has a Kronecker structure. Such a structure arises in many real networks and allows to understand the organization of complex networks. We propose a kernel-based PEM method to learn such networks. Numerical examples show the effectiveness of the proposed method. (C) 2022 Elsevier Ltd. All rights reserved.
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页数：13
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