Regularized nonparametric Volterra kernel estimation

被引：56

作者：

Birpoutsoukis, Georgios ^{[1
]}

Marconato, Anna ^{[1
]}

Lataire, John ^{[1
]}

Schoukens, Johan ^{[1
]}

机构：

[1] Vrije Univ Brussel, Dept ELEC, Pl Laan 2, B-1050 Brussels, Belgium

来源：

AUTOMATICA | 2017年 / 82卷

关键词：

System identification; Nonlinear systems; Nonparametric estimation; Volterra series; Regularization; Gaussian processes; LINEAR-SYSTEM IDENTIFICATION; REGRESSION;

D O I：

10.1016/j.automatica.2017.04.014

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, the regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modeled as Volterra series. The kernels of order higher than one, representing higher dimensional impulse responses in the series, are considered to be realizations of multidimensional Gaussian processes. Based on this, prior information about the structure of the Volterra kernel is introduced via an appropriate penalization term in the least squares cost function. It is shown that the proposed method is able to deliver accurate estimates of the Volterra kernels even in the case of a small amount of data points. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：324 / 327

页数：4