Post-nonlinear blind separation of the source signals based on variational bayesian theory and MLP
Tao Fan · Zhendong yu chongji · 2010
In the traditional post-nonlinear blind separation model for the sources,the post-nonlinearities is always required to be invertible functions.However,in practice not all models can suffice to this condition.In order to overcome this deficiency,combining Bayesian inferring with MLP network.a new improvement in MLP post-nonlinear model was proposed.In the proposed method,the post-nonlinearities is modeled with multi-layer perception(MLP) network,which also works for non-invertible post-nonlinearities.The simulation and experiment results show that the proposed method is very effective and has good robustness.