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Volumn 15, Issue PART 1, 2009, Pages 528-533

Nonlinear system identification via Gaussian regression and mixtures of kernels

Author keywords

[No Author keywords available]

Indexed keywords

AUTOCOVARIANCES; GAUSSIAN RANDOM FIELDS; GAUSSIAN REGRESSION; HYPERPARAMETERS; KERNEL STRUCTURE; MARGINAL LIKELIHOOD; MIXTURE OF GAUSSIANS; NON-LINEAR MODEL; NONPARAMETRIC APPROACHES; VARIATIONAL PROBLEMS;

EID: 80051638202     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20090706-3-FR-2004.0138     Document Type: Conference Paper
Times cited : (1)

References (21)
  • 2
    • 0024685547 scopus 로고
    • Identification of MIMO non-linear systems using a forward-regression orthogonal estimator
    • S.A. Billings, A. Chen, and M.J. Korenberg. Identification of MIMO non-linear systems using a forward-regression orthogonal algorithm. Intern. J. of Control, 49:2157-2189, 1989. (Pubitemid 20610944)
    • (1989) International Journal of Control , vol.49 , Issue.6 , pp. 2157-2189
    • Billings, S.A.1    Chen, S.2    Korenberg, M.J.3
  • 5
    • 0025464263 scopus 로고
    • Structure identification of nonlinear systems-a survey
    • R. Haber and H. Unbehauen. Structure identification of nonlinear systems-a survey. Automatica, 26:651-677, 1990.
    • (1990) Automatica , vol.26 , pp. 651-677
    • Haber, R.1    Unbehauen, H.2
  • 6
    • 33646241633 scopus 로고    scopus 로고
    • Learning long-term dependencies in NARX recurrent neural networks
    • PII S1045922796074607
    • T. Lin, B.G. Horne, P. Tino, and C.L. Giles. Learning long- term dependencies in narx recurrent neural networks. IEEE Trans. on Neural Networks, 7:1329-1338, 1996. (Pubitemid 126780474)
    • (1996) IEEE Transactions on Neural Networks , vol.7 , Issue.6 , pp. 1329-1338
    • Lin, T.1    Horne, B.G.2    Tino, P.3    Giles, C.L.4
  • 7
    • 37849012165 scopus 로고    scopus 로고
    • Regressor and structure selection in NARX models using a structured ANOVA approach
    • I. Lind and L. Ljung. Regressor and structure selection in NARX models using a structured ANOVA approach. Automatica, 44:383-395, 2008.
    • (2008) Automatica , vol.44 , pp. 383-395
    • Lind, I.1    Ljung, L.2
  • 12
    • 0347592205 scopus 로고    scopus 로고
    • An identification algorithm for polynomial narx models based on simulation error minimization
    • L. Piroddi and W. Spinelli. An identification algorithm for polynomial narx models based on simulation error minimization. Intern. J. of Control, 76:1767-1781, 2003.
    • (2003) Intern J. of Control , vol.76 , pp. 1767-1781
    • Piroddi, L.1    Spinelli, W.2
  • 16
    • 34547455409 scopus 로고    scopus 로고
    • Learning theory estimates via in- tegral operators and their approximations
    • S. Smale and D.X. Zhou. Learning theory estimates via in- tegral operators and their approximations. Constructive approximation, 26:153-172, 2007.
    • (2007) Constructive Approximation , vol.26 , pp. 153-172
    • Smale, S.1    Zhou, D.X.2
  • 18
    • 27644473446 scopus 로고    scopus 로고
    • On the role of prefiltering in nonlinear system identification
    • DOI 10.1109/TAC.2005.856655
    • W. Spinelli, L. Piroddi, and M. Lovera. On the role of prefiltering in nonlinear system identification. IEEE Trans. on Automatic Control, 50:1597-1602, 2005. (Pubitemid 41555607)
    • (2005) IEEE Transactions on Automatic Control , vol.50 , Issue.10 , pp. 1597-1602
    • Spinelli, W.1    Piroddi, L.2    Lovera, M.3
  • 19
    • 33947372892 scopus 로고    scopus 로고
    • An explicit description of the reproducing Kernel Hilbert spaces of Gaussian RBF kernels
    • DOI 10.1109/TIT.2006.881713
    • I. Steinwart, D. Hush, and C. Scovel. An explicit de- scription of the Reproducing Kernel Hilbert Space of Gaussian rbf kernels. IEEE Transactions on Informa- tion Theory, 52:4635-4643, 2006. (Pubitemid 46445299)
    • (2006) IEEE Transactions on Information Theory , vol.52 , Issue.10 , pp. 4635-4643
    • Steinwart, I.1    Hush, D.2    Scovel, C.3
  • 21
    • 0039813137 scopus 로고    scopus 로고
    • Gaussian regression and optimal finite dimensional lin- ear models
    • C. M. Bishop, editor. Springer-Verlag
    • H. Zhu, C. K. I. Williams, R. Rohwer, and M. Morciniec. Gaussian regression and optimal finite dimensional lin- ear models. In C.M. Bishop, editor, Neural networks and machine learning. Springer-Verlag, 1998.
    • (1998) Neural Networks and Machine Learning
    • Zhu, H.1    Williams, C.K.I.2    Rohwer, R.3    Morciniec, M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.