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Volumn 36, Issue 7, 2007, Pages 909-925

Non-linear system identification of the versatile-typed structures by a novel signal processing technique

Author keywords

Model selection; Non linear; Structural identification; Support vector regression

Indexed keywords

IDENTIFICATION (CONTROL SYSTEMS); SIGNAL PROCESSING; SUPPORT VECTOR MACHINES;

EID: 34249101544     PISSN: 00988847     EISSN: 10969845     Source Type: Journal    
DOI: 10.1002/eqe.660     Document Type: Article
Times cited : (7)

References (31)
  • 1
    • 0021593866 scopus 로고
    • Structural identification by extended Kalman filter
    • Hoshiya M. Structural identification by extended Kalman filter. Journal of Engineering Mechanics 1984; 110: 1757-1770.
    • (1984) Journal of Engineering Mechanics , vol.110 , pp. 1757-1770
    • Hoshiya, M.1
  • 2
    • 0033924442 scopus 로고    scopus 로고
    • Structural identification of frames under earthquake loading - time domain identification algorithms
    • Loh CH, Lin CY, Huang CC. Structural identification of frames under earthquake loading - time domain identification algorithms. Journal of Engineering Mechanics 2000; 126:693-703.
    • (2000) Journal of Engineering Mechanics , vol.126 , pp. 693-703
    • Loh, C.H.1    Lin, C.Y.2    Huang, C.C.3
  • 3
    • 0032207048 scopus 로고    scopus 로고
    • ∞ filter: Its application to structural identification
    • ∞ filter: its application to structural identification. Journal of Engineering Mechanics 1998; 124:1233-1240.
    • (1998) Journal of Engineering Mechanics , vol.124 , pp. 1233-1240
    • Sato, T.1    Qi, K.2
  • 4
    • 28244444895 scopus 로고    scopus 로고
    • Health monitoring algorithm by the Monte-Carlo filter based on non-Gaussian noise
    • Yoshida I, Sato T. Health monitoring algorithm by the Monte-Carlo filter based on non-Gaussian noise. Journal of Natural Disaster Science 2002; 24:101-107.
    • (2002) Journal of Natural Disaster Science , vol.24 , pp. 101-107
    • Yoshida, I.1    Sato, T.2
  • 5
    • 0033737832 scopus 로고    scopus 로고
    • Substructural identification using neural networks
    • Yun CB, Bahng EY. Substructural identification using neural networks. Computers and Structures 2000; 77:41-52.
    • (2000) Computers and Structures , vol.77 , pp. 41-52
    • Yun, C.B.1    Bahng, E.Y.2
  • 8
    • 0016939287 scopus 로고
    • Method of random vibration of hysteretic systems
    • Wen YK. Method of random vibration of hysteretic systems. Journal of Engineering Mechanical Division 1976; 102:249-263.
    • (1976) Journal of Engineering Mechanical Division , vol.102 , pp. 249-263
    • Wen, Y.K.1
  • 9
    • 3042512063 scopus 로고    scopus 로고
    • Analysis and modification of Volterra/Wiener neural networks for the adaptive identification of non-linear hysteretic dynamic systems
    • Pei JS, Smyth AW, Kosmatopoulos EB. Analysis and modification of Volterra/Wiener neural networks for the adaptive identification of non-linear hysteretic dynamic systems. Journal of Sound and Vibration 2004; 275:693-718.
    • (2004) Journal of Sound and Vibration , vol.275 , pp. 693-718
    • Pei, J.S.1    Smyth, A.W.2    Kosmatopoulos, E.B.3
  • 10
    • 0028977380 scopus 로고
    • Identification of hysteretic oscillators under earthquake loading by nonparametric models
    • Benedettini F, Capecchi D, Vestroni F. Identification of hysteretic oscillators under earthquake loading by nonparametric models. Journal of Engineering Mechanics 1995; 121:606-612.
    • (1995) Journal of Engineering Mechanics , vol.121 , pp. 606-612
    • Benedettini, F.1    Capecchi, D.2    Vestroni, F.3
  • 11
    • 0030107486 scopus 로고    scopus 로고
    • Identification of dynamic properties of isolated structures
    • Tan RY, Weng IW. Identification of dynamic properties of isolated structures. Engineering Structures 1996; 18:240-246.
    • (1996) Engineering Structures , vol.18 , pp. 240-246
    • Tan, R.Y.1    Weng, I.W.2
  • 12
    • 0035935966 scopus 로고    scopus 로고
    • Identification of hysteretic systems using the differential evolution algorithm
    • Kyprianou A, Worden KK, Panet M. Identification of hysteretic systems using the differential evolution algorithm. Journal of Sound and Vibration 2001; 248:289-314.
    • (2001) Journal of Sound and Vibration , vol.248 , pp. 289-314
    • Kyprianou, A.1    Worden, K.K.2    Panet, M.3
  • 13
  • 14
    • 0032487707 scopus 로고    scopus 로고
    • Identification of non-linear hysteretic isolators from periodic vibration tests
    • Ni YQ, Ko JM, Wong CW. Identification of non-linear hysteretic isolators from periodic vibration tests. Journal of Sound and Vibration 1998; 217:737-756.
    • (1998) Journal of Sound and Vibration , vol.217 , pp. 737-756
    • Ni, Y.Q.1    Ko, J.M.2    Wong, C.W.3
  • 16
    • 0025453740 scopus 로고
    • Sequential parametric identification and response of hysteretic oscillators with random excitation
    • Roberts JB, Sadeghi AH. Sequential parametric identification and response of hysteretic oscillators with random excitation. Structural Safety 1990; 8:45-68.
    • (1990) Structural Safety , vol.8 , pp. 45-68
    • Roberts, J.B.1    Sadeghi, A.H.2
  • 18
    • 2442436686 scopus 로고    scopus 로고
    • On-line identification of non-linear hysteretic structures using an adaptive tracking technique
    • Jann JN, Yang N, Lin S. On-line identification of non-linear hysteretic structures using an adaptive tracking technique. International Journal of Non-linear Mechanics 2004; 39:1481-1491.
    • (2004) International Journal of Non-linear Mechanics , vol.39 , pp. 1481-1491
    • Jann, J.N.1    Yang, N.2    Lin, S.3
  • 21
    • 33745199865 scopus 로고    scopus 로고
    • Support vector regression for on-line health monitoring of large-scale structures
    • Zhang J, Sato T, Iai S. Support vector regression for on-line health monitoring of large-scale structures. Structural Safety 2006; 28:392-406.
    • (2006) Structural Safety , vol.28 , pp. 392-406
    • Zhang, J.1    Sato, T.2    Iai, S.3
  • 22
    • 11244288369 scopus 로고    scopus 로고
    • Automatic model selection: A new instrument for social science
    • Hendry DF, Krolzig HM. Automatic model selection: a new instrument for social science. Electoral Studies 2004; 23:525-544.
    • (2004) Electoral Studies , vol.23 , pp. 525-544
    • Hendry, D.F.1    Krolzig, H.M.2
  • 23
    • 22844442782 scopus 로고    scopus 로고
    • Automatic model selection for the optimization of SVM kernels
    • Ayat NE, Cheriet M, Suen CY. Automatic model selection for the optimization of SVM kernels. Pattern Recognition 2005; 38:1733-1745.
    • (2005) Pattern Recognition , vol.38 , pp. 1733-1745
    • Ayat, N.E.1    Cheriet, M.2    Suen, C.Y.3
  • 25
    • 0003425664 scopus 로고    scopus 로고
    • Support vector machines for classification and regression
    • Technical Report, University of Southampton
    • Gunn SR. Support vector machines for classification and regression. Technical Report, University of Southampton, 1998.
    • (1998)
    • Gunn, S.R.1
  • 27
    • 0141765796 scopus 로고    scopus 로고
    • Accurate on-line support vector regression
    • Ma J, Theiler J, Perkins S. Accurate on-line support vector regression. Neural Computation 2003; 15:2683-2703.
    • (2003) Neural Computation , vol.15 , pp. 2683-2703
    • Ma, J.1    Theiler, J.2    Perkins, S.3
  • 28
    • 0346250790 scopus 로고    scopus 로고
    • Practical selection of SVM parameters and noise estimation for SVM regression
    • Cherkassky V, Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 2004; 17:113-126.
    • (2004) Neural Networks , vol.17 , pp. 113-126
    • Cherkassky, V.1    Ma, Y.2
  • 29
    • 15844392541 scopus 로고    scopus 로고
    • Fast bootstrap methodology for regression model selection
    • Lendasse A, Simon G, Wertz V, Verleysen M. Fast bootstrap methodology for regression model selection. Neurocomputing 2005; 64:161-181.
    • (2005) Neurocomputing , vol.64 , pp. 161-181
    • Lendasse, A.1    Simon, G.2    Wertz, V.3    Verleysen, M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.