Nuclear Norm Minimization Methods for Frequency Domain Subspace Identification

被引：0

作者：

Smith, Roy S. ^{[1
]}

机构：

[1] ETH, Swiss Fed Inst Technol, Automat Control Lab, CH-8092 Zurich, Switzerland

来源：

2012 AMERICAN CONTROL CONFERENCE (ACC) | 2012年

关键词：

SYSTEM; REALIZATION; MATRIX;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Frequency domain subspace identification is an effective means of obtaining a low-order model from frequency domain data. In the noisy data case using a singular value decomposition to determine the observable subspace has several problems: an incorrect weighting of the data in the singular values; difficulties in determining the appropriate rank; and a loss of the Hankel structure in the low-order approximation. A nuclear norm (sum of the singular values) minimization based method, using spectral constraints, is presented here and shown to be an effective technique for overcoming these problems.

引用

页码：2689 / 2694

页数：6

共 50 条

[1] Frequency Domain Subspace Identification Using Nuclear Norm Minimization and Hankel Matrix Realizations
Smith, Roy S.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2014, 59 (11) : 2886 - 2896
[2] Subspace-based spectrum estimation in frequency-domain by regularized nuclear norm minimization
Akcay, Huseyin
SIGNAL PROCESSING, 2014, 99 : 69 - 85
[3] Subspace Identification for Predictive State Representation by Nuclear Norm Minimization
Glaude, Hadrien
Pietquin, Olivier
Enderli, Cyrille
2014 IEEE SYMPOSIUM ON ADAPTIVE DYNAMIC PROGRAMMING AND REINFORCEMENT LEARNING (ADPRL), 2014, : 9 - 16
[4] Subspace-based spectrum estimation by reweighted and regularized nuclear norm minimization in frequency-domain
Akcay, Huseyin
Turkay, Semiha
PROCEEDINGS OF THE 2015 10TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, 2015, : 438 - 443
[5] Nuclear Norm Minimization Algorithms for Subspace Identification from Non-Uniformly Spaced Frequency Data
Plessen, Mogens Graf
Wood, Tony A.
Smith, Roy S.
2015 EUROPEAN CONTROL CONFERENCE (ECC), 2015, : 2032 - 2037
[6] Optimization Algorithms for Nuclear Norm Based Subspace Identification with Uniformly Spaced Frequency Domain Data
Plessen, Mogens Graf
Semeraro, Vito
Wood, Tony A.
Smith, Roy S.
2015 AMERICAN CONTROL CONFERENCE (ACC), 2015, : 1119 - 1124
[7] Nuclear Norm Minimization for Blind Subspace Identification (N2BSID)
Scobee, Dexter
Ratliff, Lillian
Dong, Roy
Ohlsson, Henrik
Verhaegen, Michel
Sastry, S. Shankar
2015 54TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2015, : 2127 - 2132
[8] Nuclear Norm Minimization via Active Subspace Selection
Hsieh, Cho-Jui
Olsen, Peder A.
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 32 (CYCLE 1), 2014, 32
[9] Positive Realness in Stochastic Subspace Identification: A Regularized and Reweighted Nuclear Norm Minimization Approach
Akcay, Huseyin
Turkay, Semiha
2015 EUROPEAN CONTROL CONFERENCE (ECC), 2015, : 1760 - 1765
[10] Closed-Loop Subspace Identification with Long Data based on Nuclear Norm Minimization
Sugie, Toshiharu
Inoue, Kosei
Maruta, Ichiro
2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2017,

← 1 2 3 4 5 →