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| Post-nonlinear Blind Separation of the Source Signals Based on Variational Bayesian theory and MLP |
Fan Tao 1, 2 ; Li Zhinong 1, 2; Yue Xiuting 2
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| 1. Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang, 3600632.School of Mechanical Engineering, Zhengzhou University, Zhengzhou 450001, China |
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Abstract In the traditional Post-nonlinear blind separation model of the sources, the post-nonlinearities is always required to be invertible functions. However, in practical not all models can suffice to this condition. In order to overcome this deficiency, combining of Bayesian inferring and MLP networks. a new improved method of MLP Post-nonlinear model is proposed. In the proposed method, the post-nonlinearities is modeled with multi-layer perception (MLP) networks, 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.
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Received: 09 April 2009
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Corresponding Authors:
Li Zhinong
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2010, Vol. 29 
