Maximum Likelihood Identification of a Continuous-Time Oscillator Utilizing Sampled Data

被引：6

作者：

Gonzalez, Karen ^{[1
]}

Coronel, Maria ^{[1
,2
]}

Carvajal, Rodrigo ^{[1
]}

Escarate, Pedro ^{[3
]}

Aguero, Juan C. ^{[1
,4
]}

机构：

[1] Univ Tecn Federico Santa Maria USM, Elect Engn Dept, Valparaiso, Chile

[2] Univ Los Andes, Elect Engn Dept, Merida, Venezuela

[3] Large Binocular Telescope Observ, Tucson, AZ USA

[4] Univ Newcastle, Sch Elect Engn & Comp Sci, Callaghan, NSW, Australia

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 15期

关键词：

Maximum Likelihood Methods; Continuous Time System Estimation; Time Series; DATA MODELS; SYSTEMS; ZEROS;

D O I：

10.1016/j.ifacol.2018.09.199

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 analyze the likelihood function corresponding to a continuous-time oscillator utilizing regular sampling. We analyze the equivalent sampled-data model for two cases i) instantaneous sampling and ii) integrated sampling. We illustrate the behavior of the log-likelihood function via numerical examples showing that it presents several local maxima. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：712 / 717

页数：6

共 50 条

[1] Identification of continuous-time MIMO systems via sampled data
Matsubara, Mitsuru
Usui, Yusuke
Sugimoto, Sueo
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2006, 2 (05): : 1119 - 1136
[2] IDENTIFICATION OF CONTINUOUS-TIME MULTIVARIABLE SYSTEMS FROM SAMPLED DATA
SINHA, NK
LASTMAN, GJ
INTERNATIONAL JOURNAL OF CONTROL, 1982, 35 (01) : 117 - 126
[3] Continuous-time Hammerstein system identification from sampled data
Greblicki, Wlodzimierz
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2006, 51 (07) : 1195 - 1200
[4] Exact identification of continuous-time systems from sampled data
Marelli, Damian
Fu, Minyue
2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL III, PTS 1-3, PROCEEDINGS, 2007, : 757 - +
[5] On the Unique Identification of Continuous-Time Autoregressive Models From Sampled Data
Kirshner, Hagai
Unser, Michael
Ward, John Paul
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (06) : 1361 - 1376
[6] A Continuous-Time Linear System Identification Method for Slowly Sampled Data
Marelli, Damian
Fu, Minyue
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2010, 58 (05) : 2521 - 2533
[7] Identification of continuous-time AR processes from unevenly sampled data
Larsson, EK
Söderström, T
AUTOMATICA, 2002, 38 (04) : 709 - 718
[8] Identification of continuous-time ARX models from irregularly sampled data
Larsson, Erik K.
Mossberg, Magnus
Soderstrom, Torsten
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2007, 52 (03) : 417 - 427
[9] On Maximum Likelihood Estimation of Continuous-Time Oscillators Modelled as Continuous-Time Autoregressive System
Coronel, M.
Gonzalez, K.
Escarate, P.
Carvajal, R.
Aguero, J. C.
IEEE LATIN AMERICA TRANSACTIONS, 2019, 17 (07) : 1214 - 1219
[10] CONTINUOUS-TIME OBSERVERS WITH DELAYED SAMPLED DATA
SALAMA, A
GOURISHANKAR, V
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1978, 23 (06) : 1107 - 1109

← 1 2 3 4 5 →