Configuring radial basis function network using fractal scaling process with application to chaotic time series prediction

Chaos, Solitons and Fractals

Volumn 22, Issue 4, 2004, Pages 757-766

  Omidvar, Abbas Erfanian ^a  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FRACTALS; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; RADIAL BASIS FUNCTION NETWORKS;

CLUSTERING; DYNAMIC MODELING; FRACTAL SCALING PROCESS; HYPERPLANES;

CHAOS THEORY;

EID: 2442505027     PISSN: 09600779     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chaos.2004.03.008     Document Type: Article

References (17)

1. Acosta F.M.A. Radial basis function and related models: an overview. Signal Proc. 45:1995;37-58.
2. Park J., Sandberg I.W. Universal approximation using radial-basis- function. Neural Comput. 3:1991;247-257.
3. Park J., Sandberg I.W. Approximation and radial-basis-function networks. Neural Comput. 5:1993;305-316.
4. Chen S., Cowan C.F.N., Grant P.M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Networks. 2:1991;302-309.
5. Lee S., Kil R.M. A Gaussian potential function network with hierarchically self-organizing learning. Neural Networks. 4:1991;207-224.
6. Musavi M.T., Ahmed W., Chen K.H., Faris K.B., Hummels D.M. On the training of radial basis function classifiers. Neural Networks. 5:1992;595-603.
7. Platt J. A resource-allocating network for function interpolation. Neural Comput. 3:1991;213-225.
8. Kadirkamanathan V., Niranjan M. A function estimation approach to sequential learning with neural network. Neural Comput. 5:1993;954-975.
9. Yingwei L., Sundararajan N., Saratchandran P. Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans. Neural Networks. 9:1998;308-318.
10. Roy A., Govil S., Miranda R. A neural-network learning theory and a polynomial time RBF algorithm. IEEE Trans. Neural Networks. 8:1997;1301-1313.
11. Casdagli M. Nonlinear prediction of chaotic time series. Phys. D. 35:1989;335-356.
12. Argyris J., Faust G., Haase M. An exploration of chaos. 1994;Elsevier Science BV, The Netherlands.
13. Hayken S. Adaptive filter theory. 1996;Prentice-Hall, Inc. New Jersey.
14. Lapedes A, Farber R. Nonlinear signal processing using neural networks: prediction and system modeling. Los Alamos Nat. Lab Tech. Rep. LA-UR-87-2262, 1987.
15. McDonnell J.R., Waagen D. Evolving recurrent perceptrons for time-series modeling. IEEE Trans. Neural Networks. 5:1994;24-38.
16. Moody J., Darken C.J. Fast learning in networks of locally-tuned processing units. Neural Comput. 1(2):1989;281-294.
17. Takens F. Detecting strange attractors in turbulence. Rand D.A., Young L.S. Dynamical systems and turbulence. 1980;Springer-Verlag, New York.