International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 3, No. 1, 8 Feb. 2011
Cover page and Table of Contents: PDF (size: 282KB)
Fast Time-varying modal parameter identification algorithm based on two-layer linear neural network learning for subspace tracking
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Author(s)
Kai Yang
1,*
Kaiping Yu
1
Index Terms
Subspace tracking, time-varying modal parameter, identification algorithm, neural network learning
Abstract
The key of fast identification algorithm of time-varying modal parameter based on subspace tracking is to find efficient and fast subspace-tracking algorithm. This paper presents a modified version of NIC(Novel Information Criterion) adopted in two-layer linear neural network learning for subspace tracking, which is applied in time-varying modal parameter identification algorithm based on subspace tracking and get a new time-varying modal parameter identification algorithm. Comparing with the original subspace-tracking algorithm, there is no need to set a key control parameter in advance. Simulation experiments show that new time-varying modal parameter identification algorithm has a faster convergence in the initial period and a real experiment under laboratory conditions confirms further its validity of the time-varying modal identification algorithm presented in this paper.
Cite This Paper
Kai Yang, Kaiping Yu, "Fast Time-varying modal parameter identification algorithm based on two-layer linear neural network learning for subspace tracking", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.3, no.1, pp.16-22, 2011. DOI:10.5815/ijieeb.2011.01.03
Reference
[1]Zou Jingxiang, Yu Kaiping and Pang Shiwei. Methods of time-varying structural parameter identification[J]. Advances in mechanics, 2000, 30(3):370-377
[2]Kefu Liu . Extension of modal analysis to linear time-varying system[J] . Journal of Sound and Vibration, 1999, 226(1):149-167
[3]Kefu Liu . Identification of linear time-varying system[J] . Journal of Sound and Vibration, 1997,206(4):487-505
[4]Juang J N and Pappa R S . An eigensystem realization algorithm for modal parameter identification and model reduction[J] . Journal of Guidance, Control and Dynamic, 1985,8(5):620-627
[5]Longman R W and Juang J N . Recursive form of the Eigensystem Realization algorithm for system identification[J] . Journal of Guidance, Control and Dynamic, 1989, 12(5):647-652
[6]Yu Kaiping, Xie Lili, Fan Jiuming et.al . A parameter identification of simple supported beams system carrying a moving mass[J] . Earthquake engineering and engineering vibration, 2002, 22(5):14-17
[7]Pang Shiwei, Yu Kaiping and Zou Jingxiang . Improved subspace method with application in linear tie-varying structural modal parameter indentification[J] . Chinese Journal of applied mechanics, 2005, 22(2):184-189
[8]Yang Lifang, Yu Kaiping, Pang Shiwei et. al . Comparison study on identification methods applied to linear time-varying structures[J] . Journal of Vibration and Shock, 2007,26(3):8-12
[9]F Tasker, A Bosse, S Fisher . Real-time modal parameter estimation using subspace methods:theory[J] . Mechanical Systems and Signal Processing,1998,12(6):797-808
[10]A Bosse, F Tasker, S Fisher . Real-time modal parameter estimation using subspace methods: application[J] . Mechanical Systems and Signal Processing, 1998, 12(6):809-823
[11]E M Dowling, L P Ammann and R D Degroat . A TQR-iteration based adaptive SVD for real time angel and frequency tracking[J] . IEEE Transactions on Signal Processing, 1994,42(4):914-926
[12]Wu Riqiang, Yu Kaiping and Zou Jingxiang . An improved subspace method and its application to parameter identification of time-varying structures[J] . Engineering Mechaics，2002，19(4):67-70
[13]Michel Verhaegen , Xiaode Yu . A class of subspace model identification algorithms to identify periodically and arbitrarily time-varying systems[J] . Automatica, 1995, 31:201-216
[14]Hiroshi Oku and Hidenori Kimura . Recursive 4SID algorithms using gradient type subspace tracking[J] . Automatica, 2002,38:1035-1043
[15]Pang Shiwei, Yu Kaiping and Zou Jingxiang. A projection approximation recursive subspace method for identification of modal parameter of time-varying structures [J]. Engineering Mechanics, 2002, 22(5):115-119
[16]Pang Shiwei, Yu Kaiping and Zou Jingxiang . Time-varying system identification using recursive subspace method based on free response data[J] . Journal of Vibration Engineering,2005, 18(2):233-237
[17]Bin Yang . Projection Approximation subspace tracking[J] . IEEE Transactions of Signal Processing, 1995, 43(1):95-107
[18]K Abed-Meraim, A Chkeif and Y Hua . Fast orthonormal PAST algorithm[J] . IEEE Signal Processing Letters, 2000, 7(3):60-62
[19]Bin Yang . An extension of the PASTd algorithm to both rank and subspace tracking[J] . IEEE Signal Processing Letters, 1995, 2(9):179-182
[20]Imran Ali, Doug Nyun Kim and Taikyeong Ted Jeong . A new subspace tracking algorithm using approximation of Gram-Schmidt procedure . In: 2009 International Conference on Information and Multimedia Technology, 2009, 245-248
[21]Yingbo Hua, Yong Xiang, Tianping Chen and so on . A new look at the power method for fast subspace tracking [J]. Digital Signal Processing, 1999,9:297-314
[22]Yingbo Hua, Yong Xiang, Tiangping Chen, Karim Abedmeraim et al . Natural power method for fast subspace tracking . In:Proceeding of the 1999 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing, 1999, 176-185
[23]Roland Badeau, Bertrand David and Gael Richard . Fast approximated power iteration subspace tracking[J] . IEEE Transactions on Signal Processing, 2005, 53(8):2391-2341
[24]Yongfeng Miao and Yingbo Hua . Fast Subspace Tracking by a Novel Information Criterion . Signals, Systems and Computer, the Thirty-First Asilomar Conference, 1997, vol2:1312-1316
[25]Yongfeng Miao and Yingbo Hua . Fast Subspace Tracking and Neural Network Learing by a Novel Information Criterion . IEEE Transactions on Signal Processing, 1998,46(7):1967-1979
[26]Amir Valizadeh, Mehdi Farrokhrooz and Ali Rafiei . Fast Signal Subspace Tracking Using Two-Layer Linear Neural Network Learning. 16th Mediterranean Conference on Control and Automation . France, 2008:1828-1832
[27]E Esmailzadeh and M Ghorashi . Vibraton analysis of beams traversed by uniform partially distributed moving masses[J] . Journal of Sound and Vibration, 1995, 184(1):9-17