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Generalized Spectrum Analysis by Means of Neural Networks

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Part of the book series: Advances in Soft Computing ((AINSC,volume 19))

Abstract

The problem of determination of generalized signal spectrum using neural networks has been investigated. The paper contains the formalization of the problem and is focused on building of a neural network that may be used to calculate coefficients of the generalized Fourier series for various base functions. The theoretic considerations have been illustrated by an example of analysis of a signal belonging to the Hilbert space of signals with finite energy for Laguerre’s base and a wavelet base.

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References

  1. Ayer S, Schroeter P, Brigger P (1994) Time-varying motion estimation using orthogonal polynomials and applications. Proceedings of 12-th International Conference on Pattern Recognition, Jerusalem, Israel, pp 409–414

    Google Scholar 

  2. Boyce JF, Murray LR (1989) Signal analysis in seismic studies. Advances in Electronics and Electron Physics 77: 209–318

    Article  Google Scholar 

  3. Cichocki A, Unbehauen R (1993) Neural networks for optimization and signal processing. J.Wiley, New York

    MATH  Google Scholar 

  4. Harmuth HF (1992) Transmission of information by orthogonal functions. Springer-Verlag, New York

    Google Scholar 

  5. Juszczyk W, Zajac M (1994) Fault diagnosis methods in selected synchronous drive system. Proceedings of 9-th Symposium on Basic Problems in Power Electronics and Electromechanics, Wisla, Poland, pp 170–175 (in Polish)

    Google Scholar 

  6. Mandziuk J (2000) Hopfield neural networks. Exit, Warsaw (in Polish)

    Google Scholar 

  7. Philips W, Jonghe G (1992) Data compression of ECGs by high degree polynomial approximation. IEEE Trans. Biomedical Eng. 39: 330–337

    Google Scholar 

  8. Popularkis AD (1996) The transforms and applications handbook. CRC and IEEE Press, Boca Raton, Florida

    Google Scholar 

  9. Soliman SS, Srinath MD (1998) Continuous and discrete signals and systems. Prentice-Hall, Upper Saddle River, New Jersey

    Google Scholar 

  10. Yaroslaysky L, Eden H (1996) Fundamentals of digital optics. Digital signal processing in optics and holography. Birkhauser, Boston

    Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Grabowski, D., Walczak, J. (2003). Generalized Spectrum Analysis by Means of Neural Networks. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_109

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  • DOI: https://doi.org/10.1007/978-3-7908-1902-1_109

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-0005-0

  • Online ISBN: 978-3-7908-1902-1

  • eBook Packages: Springer Book Archive

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