Abstract
Lower order alpha stable distribution processes can model the impulsive random signals and noises well in physical observation. Conventional blind source separation is based on second order statistics (SOS). In this paper, we propose neural network structures related to multilayer feedforward networks for performing blind source separation based on the fractional lower order statistics (FLOS). The simulation results and analysis show that the proposed networks and algorithms are robust.
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© 2004 Springer-Verlag Berlin Heidelberg
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Zha, D., Qiu, T., Tang, H., Sun, Y., Li, S., Shen, L. (2004). A New Blind Source Separation Method Based on Fractional Lower Order Statistics and Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_111
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DOI: https://doi.org/10.1007/978-3-540-28647-9_111
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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