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Volumn 64, Issue 5, 1996, Pages 829-837

Regularized orthogonal least squares algorithm for constructing radial basis function networks

Author keywords

[No Author keywords available]

Indexed keywords

LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; NEURAL NETWORKS; REGRESSION ANALYSIS;

EID: 0030195189     PISSN: 00207179     EISSN: 13665820     Source Type: Journal    
DOI: 10.1080/00207179608921659     Document Type: Article
Times cited : (208)

References (16)
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    • Barron, A.R.1    Xiao, X.2
  • 2
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    • Improving the generalization properties of radial basis function neural networks
    • Bishop, C., 1991, Improving the generalization properties of radial basis function neural networks. Neural Computation, 3, 579-588.
    • (1991) Neural Computation , vol.3 , pp. 579-588
    • Bishop, C.1
  • 4
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    • Radial basis functions for signal prediction and system modelling
    • Chen, S., 1994, Radial basis functions for signal prediction and system modelling. Journal of Applied Science and Computations, 1.
    • (1994) Journal of Applied Science and Computations , pp. 1
    • Chen, S.1
  • 5
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    • Modelling and analysis of non-linear time series
    • Chen, S., and Billings, S. A., 1989, Modelling and analysis of non-linear time series. International Journal of Control, 50, 2151-2171.
    • (1989) International Journal of Control , vol.50 , pp. 2151-2171
    • Chen, S.1    Billings, S.A.2
  • 6
    • 0026116468 scopus 로고
    • Orthogonal least squares learning algorithm for radial basis function networks
    • Chen, S., Cowan, C. F. N., and Grant, P. M., 1991, Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, 2, 302-309.
    • (1991) IEEE Transactions on Neural Networks , vol.2 , pp. 302-309
    • Chen, S.1    Cowan, C.F.N.2    Grant, P.M.3
  • 7
    • 0026992119 scopus 로고
    • Orthogonal least squares algorithm for training multi-output radial basis function networks
    • Pt F
    • Chen, S., Grant, P. M., and Cowan, C. F. N., 1992, Orthogonal least squares algorithm for training multi-output radial basis function networks. Proceedings of the Institution of Electrical Engineers Pt F, 139, 378-384.
    • (1992) Proceedings of the Institution of Electrical Engineers , vol.139 , pp. 378-384
    • Chen, S.1    Grant, P.M.2    Cowan, C.F.N.3
  • 8
    • 32044449925 scopus 로고
    • Generalized cross-validation as a method for choosing a good ridge parameter
    • Golub, G. H., Heath, M., and Wahba, G., 1979, Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 1, 215-223.
    • (1979) Technometrics , vol.1 , pp. 215-223
    • Golub, G.H.1    Heath, M.2    Wahba, G.3
  • 10
    • 84942484786 scopus 로고
    • Ridge regression: Biased estimation for nonorthogonal problems
    • Hoerl, A. E., and Kennard, R. W., 1970, Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 12, 55-67.
    • (1970) Technometrics , vol.12 , pp. 55-67
    • Hoerl, A.E.1    Kennard, R.W.2
  • 11
    • 84947630316 scopus 로고
    • ASMOD: An algorithm for adaptive spline modelling of observation data
    • Kavli, T., 1993, ASMOD: an algorithm for adaptive spline modelling of observation data. International Journal of Control, 58, 947-968.
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    • Kavli, T.1
  • 12
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    • Bayesian interpolation
    • MacKay, D. J. C., 1992, Bayesian interpolation. Neural Computation, 4, 415-447.
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    • Mackay, D.J.C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.