PREFILTERING BASED ON WOLD'S DECOMPOSITION FOR LINEAR MIMO SYSTEM IDENTIFICATION

被引:0
|
作者
Matsubara, Mitsuru [1 ]
Fujimoto, Hisaki [1 ]
Morita, Jin [1 ]
Sugimoto, Sueo [1 ]
机构
[1] Ritsumeikan Univ, Dept Elect & Elect Engn, Kusatsu, Shiga 5258577, Japan
关键词
System identification; ORT method; Wold's decomposition; LQ decomposition; Prefiltering; FIR filter;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we present a prefiltering method for linear multi-input multi-output (MIMO) system identification. It is assumed that the system representation is the combined system with deterministic system and stochastic system based on orthogonal decomposition of the output process. The stochastic component can be defined clearly by the orthogonal decomposition of the output process based on the conception of Wold's decomposition. Therefore, we consider removing the stochastic component front the output process. If the stochastic component can be removed completely, by employing deterministic subspace methods, the parametrization. problem included in the state-space model identification is completely bypassed. Also, if the stochastic component can be estimated precisely, this namely means a prefiltering for the system identification. For implementing this purpose, we employ LQ decomposition, also consider that the last block row of L-matrix is extracted. Also, the effectivities are shown in numerical experiments.
引用
收藏
页码:41 / 55
页数:15
相关论文
共 50 条
  • [1] Blind MIMO system identification based on cumulant subspace decomposition
    Liang, J
    Ding, Z
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2003, 51 (06) : 1457 - 1468
  • [2] Linear System Identification Based on a Kronecker Product Decomposition
    Paleologu, Constantin
    Benesty, Jacob
    Ciochina, Silviu
    IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2018, 26 (10) : 1793 - 1808
  • [3] On the role of prefiltering in nonlinear system identification
    Spinelli, W
    Piroddi, L
    Lovera, M
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2005, 50 (10) : 1597 - 1602
  • [4] Statistical prefiltering for MIMO systems with linear receivers in the presence of transmit correlation
    Kiessling, M
    Speidel, J
    Viering, I
    Reinhardt, M
    57TH IEEE VEHICULAR TECHNOLOGY CONFERENCE, VTC 2003-SPRING, VOLS 1-4, PROCEEDINGS, 2003, : 267 - 271
  • [5] Identification of Wiener System Using Iterative Prefiltering-based Algorithm
    Saini, Vikram
    Dewan, Lillie
    2017 INTERNATIONAL CONFERENCE ON INNOVATIONS IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND MEDIA TECHNOLOGY (ICIEEIMT), 2017, : 92 - 97
  • [6] Linear System Identification Based on a Third-Order Tensor Decomposition
    Benesty, Jacob
    Paleologu, Constantin
    Ciochin, Silviu
    IEEE SIGNAL PROCESSING LETTERS, 2023, 30 : 503 - 507
  • [7] RELAY FEEDBACK RESPONSE BASED IDENTIFICATION OF LINEAR 2-BY-2 MIMO SYSTEM
    Sujatha, Vijayaraghavan
    Panda, Rames Chandra
    CONTROL AND INTELLIGENT SYSTEMS, 2013, 41 (02) : 85 - 90
  • [8] Computer experiments in system identification using a prefiltering bank
    Alexiu, Mugur
    Aiordachioaie, Dorel
    ISSCS 2007: INTERNATIONAL SYMPOSIUM ON SIGNALS, CIRCUITS AND SYSTEMS, VOLS 1 AND 2, 2007, : 409 - +
  • [9] Blind MIMO system identification using PARAFAC decomposition of an output HOS-based tensor
    Acar, TD
    Petropulu, AP
    CONFERENCE RECORD OF THE THIRTY-SEVENTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, VOLS 1 AND 2, 2003, : 1080 - 1084
  • [10] Controlling a Linear MIMO System by a Measurement Vector Using Multilevel Decomposition
    N. E. Zubov
    E. A. Mikrin
    V. N. Ryabchenko
    Journal of Computer and Systems Sciences International, 2020, 59 : 151 - 160