DocumentCode :
2567992
Title :
Nonparametric sparse estimators for identification of large scale linear systems
Author :
Chiuso, Alessandro ; Pillonetto, Gianluigi
Author_Institution :
Dipt. di Tec. e Gestione dei Sist. Industriali, Univ. of Padova, Vicenza, Italy
fYear :
2010
fDate :
15-17 Dec. 2010
Firstpage :
2942
Lastpage :
2947
Abstract :
Identification of sparse high dimensional linear systems pose sever challenges to off-the-shelf techniques for system identification. This is particularly so when relatively small data sets, as compared to the number of inputs and outputs, have to be used. In this paper we introduce a new nonparametric technique which borrows ideas from a recently introduced Kernel estimator called “stable-spline” as well as from sparsity inducing priors which use ℓ1 penalty. We compare the new method with a group LAR-type of algorithm applied to estimation of sparse Vector Autoregressive models and to standard PEM methods.
Keywords :
autoregressive processes; identification; nonparametric statistics; Kernel estimator; group LAR-type; large scale linear systems; nonparametric sparse estimator; nonparametric technique; off-the-shelf techniques; sparse high dimensional linear systems; sparse vector autoregressive model; stable spline; standard PEM method; system identification; Data models; Estimation; Hidden Markov models; Kernel; Linear systems; Predictive models; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
ISSN :
0743-1546
Print_ISBN :
978-1-4244-7745-6
Type :
conf
DOI :
10.1109/CDC.2010.5717169
Filename :
5717169
Link To Document :
بازگشت