Abstract
In this work, we propose a new method for source separation of post-nonlinear mixtures that combines evolutionary-based global search, gaussianization and a local search step based on FastICA algorithm. The rationale of the proposal is to attempt to obtain efficient and precise solutions using with parsimony the available computational resources, and, as shown by the simulation results, this aim was satisfactorily fulfilled.
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Dias, T.M., Attux, R., Romano, J.M.T., Suyama, R. (2009). Blind Source Separation of Post-Nonlinear Mixtures Using Evolutionary Computation and Gaussianization. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_30
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DOI: https://doi.org/10.1007/978-3-642-00599-2_30
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