Skip to main content

Analysis of Variance of Three Contrast Functions in a Genetic Algorithm for Non-linear Blind Source Separation

  • Conference paper
Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

Included in the following conference series:

  • 3107 Accesses

Abstract

The task of recovering a set of unknown sources from another set of mixtures directly observable and little more information about the way they were mixed is called the blind source separation problem. If the assumption in order to obtain the original sources is their statistical independence, then ICA (Independent Component Analysis) may be the technique to recover the signals. In this contribution, we propose and analyze three evaluation functions (contrast functions in Independent Component Analysis terminology) for the use in a genetic algorithm (PNL-GABSS, Post-NonLinear Genetic Algorithm for Blind Source Separation) which solves source separation in nonlinear mixtures, assuming the post-nonlinear mixture model. A thorough analysis of the performance of the chosen contrast functions is made by means of ANOVA (Analysis of Variance), showing the validity of the three approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley, New York (2001)

    Book  Google Scholar 

  2. Burel, G.: Blind separation of sources: a nonlinear neural algorithm. Neural Networks 5, 937–947 (1992)

    Article  Google Scholar 

  3. Lee, T.W., Koehler, B., Orglmeister, R.: Blind source separation of nonlinear mixing models. In: Neural Networks for Signal Processing VII, pp. 406–415. IEEE Press, Los Alamitos (1997)

    Google Scholar 

  4. Hyvärinen, A., Pajunen, P.: Nonlinear independent component analysis: Existence and uniqueness results. Neural Networks 12, 429–439 (1999)

    Article  Google Scholar 

  5. Taleb, A., Jutten, C.: Source separation in post-nonlinear mixtures. IEEE Trans. on Signal Processing 47, 2807–2820 (1999)

    Article  Google Scholar 

  6. Rojas, I., Puntonet, C., Cañas, A., Pino, B., Fernández, J., Rojas, F.: Genetic algorithms for the blind separation of sources. In: IASTED (2001)

    Google Scholar 

  7. Rojas, F., Puntonet, C., Rodríguez, M., Rojas, I., Clemente, R.: Blind source separation in post-nonlinear mixtures using competitive learning, simulated annealing and a genetic algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part C 34, 407–416 (2004)

    Article  Google Scholar 

  8. Cardoso, J.F., Souloumiac, A.: Blind beamforming for non Gaussian signals. IEE Proceedings-F 140, 362–370 (1993)

    Google Scholar 

  9. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, Tercera edn. Springer, New York (1999)

    Google Scholar 

  10. Amari, S.I., Cichocki, A., Yang, H.: A new learning algorithm for blind source separation. In: Advances in Neural Information Processing Systems 8, pp. 757–763. MIT Press, Cambridge (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rojas, F., Górriz, J.M., Valenzuela, O. (2005). Analysis of Variance of Three Contrast Functions in a Genetic Algorithm for Non-linear Blind Source Separation. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_128

Download citation

  • DOI: https://doi.org/10.1007/11494669_128

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics