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Title: An extension of MISEP for post-nonlinear-linear mixture separation
Authors: Sun, Z.-L. 
Keywords: Cumulative probability function (CPF)
Information maximization (INFOMAX)
Multilayer perceptrons
Nonlinear blind source separation (BSS)
Issue Date: 2009
Citation: Sun, Z.-L. (2009). An extension of MISEP for post-nonlinear-linear mixture separation. IEEE Transactions on Circuits and Systems II: Express Briefs 56 (8) : 654-658. ScholarBank@NUS Repository.
Abstract: Mutual information separation (MISEP) is a versatile independent component analysis (ICA) algorithm that can be used to handle linear and nonlinear mixtures. By incorporating the a priori information of mixtures, an extended MISEP method is proposed in this brief to recover the source signals from the post-nonlinear-linear (PNL-L) mixtures. One group of multilayer perceptrons and two linear networks are used as the unmixing system, and another group of multilayer perceptrons is used as the auxiliary network. The learning algorithm of the system parameters is obtained by maximizing the output entropy with the gradient ascent method. Experimental results demonstrate that the proposed method is effective and efficient for PNL-L mixture separation. © 2009 IEEE.
Source Title: IEEE Transactions on Circuits and Systems II: Express Briefs
ISSN: 15497747
DOI: 10.1109/TCSII.2009.2024246
Appears in Collections:Staff Publications

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