Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model
This paper focuses on the joint estimation of parameters and time-delays of the multiple-input single-output output-error systems. Since the time-delays are unknown, an effective identification model with a high dimensional and sparse parameter vector is established based on overparameterization. Th...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-11-01
|
Series: | Algorithms |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-4893/11/11/180 |
id |
doaj-e0d8b3d34fca4b83902359435bf322c7 |
---|---|
record_format |
Article |
spelling |
doaj-e0d8b3d34fca4b83902359435bf322c72020-11-24T21:47:44ZengMDPI AGAlgorithms1999-48932018-11-01111118010.3390/a11110180a11110180Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary ModelJunyao You0Yanjun Liu1School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, ChinaSchool of Internet of Things Engineering, Jiangnan University, Wuxi 214122, ChinaThis paper focuses on the joint estimation of parameters and time-delays of the multiple-input single-output output-error systems. Since the time-delays are unknown, an effective identification model with a high dimensional and sparse parameter vector is established based on overparameterization. Then, the identification problem is converted to a sparse optimization problem. Based on the basis pursuit de-noising criterion and the auxiliary model identification idea, an auxiliary model based basis pursuit de-noising iterative algorithm is presented. The parameters are estimated by solving a quadratic program, and the unavailable terms in the information vector are updated by the auxiliary model outputs iteratively. The time-delays are estimated according to the sparse structure of the parameter vector. The proposed method can obtain effective estimates of the parameters and time-delays from few sampled data. The simulation results illustrate the effectiveness of the proposed algorithm.https://www.mdpi.com/1999-4893/11/11/180multivariable systemparameter identificationtime-delay estimationbasis pursuit de-noisingauxiliary modelquadratic program |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junyao You Yanjun Liu |
spellingShingle |
Junyao You Yanjun Liu Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model Algorithms multivariable system parameter identification time-delay estimation basis pursuit de-noising auxiliary model quadratic program |
author_facet |
Junyao You Yanjun Liu |
author_sort |
Junyao You |
title |
Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model |
title_short |
Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model |
title_full |
Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model |
title_fullStr |
Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model |
title_full_unstemmed |
Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model |
title_sort |
iterative identification for multivariable systems with time-delays based on basis pursuit de-noising and auxiliary model |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2018-11-01 |
description |
This paper focuses on the joint estimation of parameters and time-delays of the multiple-input single-output output-error systems. Since the time-delays are unknown, an effective identification model with a high dimensional and sparse parameter vector is established based on overparameterization. Then, the identification problem is converted to a sparse optimization problem. Based on the basis pursuit de-noising criterion and the auxiliary model identification idea, an auxiliary model based basis pursuit de-noising iterative algorithm is presented. The parameters are estimated by solving a quadratic program, and the unavailable terms in the information vector are updated by the auxiliary model outputs iteratively. The time-delays are estimated according to the sparse structure of the parameter vector. The proposed method can obtain effective estimates of the parameters and time-delays from few sampled data. The simulation results illustrate the effectiveness of the proposed algorithm. |
topic |
multivariable system parameter identification time-delay estimation basis pursuit de-noising auxiliary model quadratic program |
url |
https://www.mdpi.com/1999-4893/11/11/180 |
work_keys_str_mv |
AT junyaoyou iterativeidentificationformultivariablesystemswithtimedelaysbasedonbasispursuitdenoisingandauxiliarymodel AT yanjunliu iterativeidentificationformultivariablesystemswithtimedelaysbasedonbasispursuitdenoisingandauxiliarymodel |
_version_ |
1725895959022403584 |