A competitive neural network for blind separation of sources based on geometric properties

  • Alberto Prieto
  • Carlos G. Puntonet
  • Beatriz Prieto
  • Manuel Rodríguez-Alvarez
Neural Networks for Perception
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1240)

Abstract

This contribution presents a new approach to recover original signals (“sources”) from their linear mixtures, observed by the same number of sensors. The algorithm proposed assume that the input distributions are bounded and the sources generate certain combinations of boundary values. The method is simpler than other proposals and is based on geometric algebra properties. We present a neural network approach to show that with two networks, one for the separation of sources and one for weight learning, running in parallel, it is possible to efficiently recover the original signals. The learning rule is unsupervised and each computational element uses only local information.

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References

  1. [1]
    S. Amari, A. Cichocki, H.H. Yang, “A new learning algorithm for blind signal separation”. Advances in Neural Information Processing Systems (1995), Vol.8. MIT Press: Cambridge, MA, pp. 757–763, 1996.Google Scholar
  2. [2]
    A. J. Bell, T. J. Sejnowski, “An information-maximisation approach to blind separation and blind deconvolution”. Neural Computation 7, pp. 1129–1159. 1995.Google Scholar
  3. [3]
    C. Jutten, J. Hérault, P. Comon and E. Sorouchiary, “Blind separation of sources”, Parts I, II and III. Signal Processing, vol. 24, no. 1, pp.1–29, July 1991.Google Scholar
  4. [4]
    K. Matsuoka, M. Ohya, M. Kawamoto, “A neural net for blind separation of nonstationary signals”. Neural Networks, vol. 8, no. 3, pp. 411–419, 1995.Google Scholar
  5. [5]
    A.M. Peinado, J.M. Lopez, V.E. Sánchez, J.C. Segura and A.J. Rubio, “Improvements in HMM-based isolated word recognition systems”, IEE Proceedings-I, Vol. 138, No.3, pp.201–206, June, 1991.Google Scholar
  6. [6]
    C. G. Puntonet, A. Prieto, C. Jutten, M. Rodríguez-Alvarez, J. Ortega, “Separation of sources: a geometry-based procedure for reconstruction of n-valued signals”. Signal Processing, vol. 46, no. 3, pp. 267–284. 1995.Google Scholar
  7. [7]
    Puntonet,C.G.; Prieto,A.: “An adaptive geometrical procedure for blind separation of sources”, Neural Processing Letters, Vol.2, No.5, pp. 23–27, Sept. 1995.Google Scholar
  8. [8]
    C.G.Puntonet, A.Prieto, J. Ortega. “New geometrical approach for blind separation of sources mapped to a neural network”. Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP-96), Venecia, Italy, IEEE Computer Soc. Press, pp. 174–182, 21–23 August, 1996Google Scholar
  9. [9]
    C.G. Puntonet, A. Mansour and C. Jutten, “Un algorithme géométrique pour la séparation de sources. 15th GRETSI. Juan-Les-pins (France), Sept., 18–21, 1995.Google Scholar
  10. [10]
    Rumelhart,D.E.; Zipser,D., Feature discovery by Competitive learning. In: Parallel Distributed processing, Rumelhart,D.E. & McClelland, J.L., MIT Press;, Vol. I, 151–193, 1986.Google Scholar

Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • Alberto Prieto
    • 1
  • Carlos G. Puntonet
    • 1
  • Beatriz Prieto
    • 1
  • Manuel Rodríguez-Alvarez
    • 1
  1. 1.Departamento de Electrónica y Tecnología de ComputadoresUniversidad de GranadaGranadaSpain

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