Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/243296
Type: Artigo
Title: Source separation in post-nonlinear mixtures by means of monotonic networks
Author: Duarte, Leonardo Tomazeli
Pereira, Filipe de Oliveira
Romis, Attux
Suyama, Ricardo
Romano, João M.T.
Abstract: In this work, we investigate the use of monotonic neural networks as compensating functions in the context of source separation of post-nonlinear (PNL) mixtures. We first provide a numerical example that illustrates the importance of having bijective nonlinear compensating functions in PNL models. Then, we propose a separation framework in which a monotonic neural network is considered in the first stage of the PNL separating system. Finally, numerical experiments are performed to assess the proposed framework.
In this work, we investigate the use of monotonic neural networks as compensating functions in the context of source separation of post-nonlinear (PNL) mixtures. We first provide a numerical example that illustrates the importance of having bijective nonl
Subject: Separação cega de fontes
Country: Alemanha
Editor: Springer
Citation: Source Separation In Post-nonlinear Mixtures By Means Of Monotonic Networks. Springer-verlag Berlin, v. 9237, p. 176-183 2015.
Rights: fechado
fechado
Identifier DOI: 10.1007/978-3-319-22482-4_20
Address: https://link.springer.com/chapter/10.1007/978-3-319-22482-4_20
Date Issue: 2015
Appears in Collections:FEEC - Artigos e Outros Documentos
FCA - Artigos e Outros Documentos

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