Nuclear Norms for System Identification - a direct input-output approach

被引:0
作者
Pelckmans, Kristiaan [1 ]
Cuho, Ruben [1 ]
机构
[1] UU, Uppsala, Sweden
关键词
System Identification; Convex Optimization; APPROXIMATION;
D O I
10.1016/j.ifacol.2015.12.202
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper studies a method for the identification of LTI systems based on the nuclear norm of the Hankel matrix of the model - termed NucID. The nuclear norm has been put forward as a convex proxy to a class of rank-constraints that are hard to work with. The rationale for investigating such approach is that the estimate is more exible/robust in case of low Signal-to-Noise Ratios (SNRs), and other noisy effects in the data. This paper explores the formalisation, gives numerical results and brings up other issues for stimulating the discussion on the use of such approaches. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:644 / 649
页数:6
相关论文
共 50 条
[31]   A hybrid input-output approach to model metabolic systems: An application to intracellular thiamine kinetics [J].
Bellazzi, R ;
Guglielmann, R ;
Ironi, L ;
Patrini, C .
JOURNAL OF BIOMEDICAL INFORMATICS, 2001, 34 (04) :221-248
[32]   On closed-loop system identification using polyspectral analysis given noisy input-output time-domain data [J].
Tugnait, JK ;
Zhou, Y .
AUTOMATICA, 2000, 36 (12) :1795-1808
[33]   Identification of EIV models with coloured input-output noise: combining PEM and covariance matching method [J].
Khorasani, Masoud Moravej ;
Haeri, Mohammad .
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2018, 49 (08) :1738-1747
[34]   Fuzzy identification of nonuniformly sampled nonlinear systems based on forwards recursive input-output clustering [J].
Liu, Ranran ;
Zheng, Enxing ;
Li, Feng ;
Guo, Wei ;
Jiang, Yifeng .
NEURAL COMPUTING & APPLICATIONS, 2024, 36 (05) :2315-2322
[35]   Identification of fuzzy neural networks by forward recursive input-output clustering and accurate similarity analysis [J].
Qiao, Junfei ;
Li, Wei ;
Zeng, Xiao-Jun ;
Han, Honggui .
APPLIED SOFT COMPUTING, 2016, 49 :524-543
[36]   System-level, input-output and new parameterizations of stabilizing controllers, and their numerical computation [J].
Zheng, Yang ;
Furieri, Luca ;
Kamgarpour, Maryam ;
Li, Na .
AUTOMATICA, 2022, 140
[37]   Input-output consistency in integrate and fire interconnected neurons [J].
Lansky, Petr ;
Polito, Federico ;
Sacerdote, Laura .
APPLIED MATHEMATICS AND COMPUTATION, 2023, 440
[38]   Input-Output Manifold Learning with State Space Models [J].
Tanaka, Daisuke ;
Matsubara, Takamitsu ;
Sugimoto, Kenji .
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2016, E99A (06) :1179-1187
[39]   IDENTIFICATION OF LINEAR-SYSTEMS WITH UNKNOWN TIME-DELAY AND INPUT-OUTPUT NOISY DATA - HIGH-ORDER CORRELATION APPROACH [J].
CHEN, JM ;
CHEN, BS .
CONTROL-THEORY AND ADVANCED TECHNOLOGY, 1994, 10 (03) :317-346
[40]   Analysis of the variability of joint input-output estimation methods [J].
Ninness, B ;
Hjalmarsson, H .
AUTOMATICA, 2005, 41 (07) :1123-1132