Dual-orthogonal radial basis function networks for nonlinear time series prediction

Neural Networks

Volumn 11, Issue 3, 1998, Pages 479-493

  Billings, Steve A ^a   Hong, X ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Classification function; Dual orthogonal RBF Network; Multistep ahead predictions; Network structure; OLS; Radial basis function; System identification; Time series

Indexed keywords

COMPUTER SIMULATION; DATA STRUCTURES; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; REGRESSION ANALYSIS; TIME SERIES ANALYSIS;

CLASSIFICATION FUNCTION; NETWORK STRUCTURE; RADIAL BASIS FUNCTION NEURAL NETWORK; SYSTEM IDENTIFICATION;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; LEARNING; MATHEMATICAL MODEL; PREDICTION; PRIORITY JOURNAL;

EID: 0032053321     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(97)00132-9     Document Type: Article

References (19)

1. Akaike H. A new look at the statistical model identification. IEEE Trans. Automatic Control AC-. 19:1974;716-723.
2. Barron, A.R. (1984). Predicted squared error: A criterion for automatic model selection. In J.F. Stanley (Ed.), Self-organizing Methods in Modelling GMDH Type Algorithms. (pp. 87-103). New York: Marcel Dekker, Inc.
3. Billings S.A., Chen S. Extended model set, global data and threshold model identification of severely non-linear systems. Int. J. Control. 50(5):1989;1897-1923.
4. Billings S.A., Korenberg M.J., Chen S. Identification of non-linear output-affine systems using an orthogonal least-squares algorithm. International Journal of Systems Science. 19:1988;1559-1568.
5. Billings S.A., Chen S., Korenberg M. Identification of MIMO non-linear systems using a forward-regression orthogonal estimator. International Journal of Control. 49(6):1989;2157-2189.
6. Billings S.A., Jamaluddin H.B., Chen S. Properties of neural networks with applications to modelling dynamic systems. International Journal of Control. 55(1):1992;193-224.
7. Broomhead D.S., Lowe D. Multivariable functional interpolation and adaptive networks. Complex Systems. 2:1988;321-355.
8. Casdagli M. Nonlinear prediction of chaotic time series. Physica D. 35:1989;335-356.
9. Chakravarthy S.V., Ghosh J. Scale-based clustering using the radial basis function network. IEEE Transactions on Neural Networks. 7(5):1996;1250-1261.
10. Chen S., Billings S.A., Luo W. Orthorgonal least squares methods and their applications to non-linear system identification. International Journal of Control. 50:1989;1873-1896.
11. Chng E.S., Chen S., Mulgrew B. Gradient radial basis function networks for nonlinear and nonstationary time series prediction. IEEE Transactions on Neural Networks. 7(1):1996;190-194.
12. Hastie T., Tibshirani R. Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 18:1996;607-616.
13. Klecka, W.R. (1980). Discriminant Analysis. Sage Publications.
14. Kleinbaum, D.G., Kupper, L.L. and Muller, K.E. (1987). Applied Regression Analysis and Other Multivariable Methods. Second Edition, Boston: PWS-KENT Publishing Company.
15. Liu Y. Unbiased estimate of generalisation error and model selection in neural network. Neural Networks. 8(2):1995;311-341.
16. Moody J., Darken C. Fast-learning in networks of locally-tuned processing units. Neural Computation NN-. 4:1989;740-747.
17. Poggio T., Girosi F. Network for approximation and learning. Proc. IEEE. 78(9):1990;1481-1497.
18. Powell, M.J.D. (1985). IMA Conf. on Algorithms for the Approximation of Functions and Data. RMCS Shrivenham.
19. Zheng G.L., Billings S.A. Radial basis function network configuration using mutual information and the orthogonal least squares algorithm. Neural Networks. 9(9):1996;1619-1637.