Source separation in post-nonlinear mixtures by means of monotonic networks

sem informaçãosem informaçãoIn 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 nonl9237176183FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTIFICO E TECNOLOGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTIFICO E TECNOLOGICOsem informaçãosem informaçãosem informaçãosem informação12th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA