Determining the weights of a Fourier series neural network on the basis of the multidimensional discrete Fourier transform

Krzysztof Halawa

International Journal of Applied Mathematics and Computer Science (2008)

  • Volume: 18, Issue: 3, page 369-375
  • ISSN: 1641-876X

Abstract

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This paper presents a method for training a Fourier series neural network on the basis of the multidimensional discrete Fourier transform. The proposed method is characterized by low computational complexity. The article shows how the method can be used for modelling dynamic systems.

How to cite

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Krzysztof Halawa. "Determining the weights of a Fourier series neural network on the basis of the multidimensional discrete Fourier transform." International Journal of Applied Mathematics and Computer Science 18.3 (2008): 369-375. <http://eudml.org/doc/207892>.

@article{KrzysztofHalawa2008,
abstract = {This paper presents a method for training a Fourier series neural network on the basis of the multidimensional discrete Fourier transform. The proposed method is characterized by low computational complexity. The article shows how the method can be used for modelling dynamic systems.},
author = {Krzysztof Halawa},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {orthogonal neural networks; Fourier series; fast Fourier transform; approximation; nonlinear systems},
language = {eng},
number = {3},
pages = {369-375},
title = {Determining the weights of a Fourier series neural network on the basis of the multidimensional discrete Fourier transform},
url = {http://eudml.org/doc/207892},
volume = {18},
year = {2008},
}

TY - JOUR
AU - Krzysztof Halawa
TI - Determining the weights of a Fourier series neural network on the basis of the multidimensional discrete Fourier transform
JO - International Journal of Applied Mathematics and Computer Science
PY - 2008
VL - 18
IS - 3
SP - 369
EP - 375
AB - This paper presents a method for training a Fourier series neural network on the basis of the multidimensional discrete Fourier transform. The proposed method is characterized by low computational complexity. The article shows how the method can be used for modelling dynamic systems.
LA - eng
KW - orthogonal neural networks; Fourier series; fast Fourier transform; approximation; nonlinear systems
UR - http://eudml.org/doc/207892
ER -

References

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  13. Rafajłowicz E. and Skubalska-Rafajłowicz E. (1993). FFT in calculating nonparametric regression estimate based on trigonometric series, International Journal of Applied Mathematics and Computer Science 3(4): 713-720. Zbl0802.65145
  14. Sher C. F., Tseng C. S. and Chen, C. (2001). Properties and performance of orthogonal neural network in function approximation, International Journal of Intelligent Systems 16(12): 1377-1392. Zbl0997.68105
  15. Tseng C. S. and Chen C. S. (2004). Performance comparison between the training method and the numerical method of the orthogonal neural network in function approximation, International Journal of Intelligent Systems 19(12): 1257-1275. Zbl1101.68753
  16. Van Loan C. (1992). Computational Frameworks for the Fast Fourier Transform, SIAM, Philadelphia, PA. Zbl0757.65154
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  18. Zhu C., Shukla D. and Paul, F. (2002). Orthogonal functions for system identification and control, in: C.T. Leondes (Ed.), Neural Networks Systems, Techniques and Apllications: Control and Dynamic Systems, Academic Press, San Diego, CA, pp. 1-73. 

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