Abstract
This paper proposes a very fast method for blindly initializing a nonlinear mapping which transforms a sum of random variables. The method provides a surprisingly good approximation even when the basic assumption is not fully satisfied. The method can been used successfully for initializing nonlinearity in post-nonlinear mixtures or in Wiener system inversion, for improving algorithm speed and convergence.
This work has been partly funded by the European project BLInd Source Separation and applications (BLISS, IST 1999-14190), by the Direcció General de Recerca de la Generalitat de Catalunya under a grant for Integrated Actions ACI2001, and by the Universitat de Vic under the grant R0912
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Sole-Casals, J., Jutten, C., Pham, DT. (2003). Initialisation of Nonlinearities for PNL and Wiener systems Inversion. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_29
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DOI: https://doi.org/10.1007/3-540-44869-1_29
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