A Non-Linear Blind Source Separation Method Based on Perceptron Structure and Conjugate Gradient Algorithm

作者：Li, Wei^*; Yang, Huizhong

来源：Circuits, Systems, and Signal Processing, 2014, 33(11): 3573-3590.

DOI：10.1007/s00034-014-9818-1

• Non-linear mixtures
• Blind signal separation
• Score function estimation
• Conjugate gradient algorithm

The linear mixing model has been considered previously in most of the researches which are devoted to the blind source separation (BSS) problem. In practice, a more realistic BSS mixing model should be the non-linear one. In this paper, we propose a non-linear BSS method, in which a two-layer perceptron network is employed as the separating system to separate sources from observed non-linear mixture signals. The learning rules for the parameters of the separating system are derived based on the minimum mutual information criterion with conjugate gradient algorithm. Instead of choosing a proper non-linear functions empirically, the adaptive kernel density estimation is used in order to estimate the probability density functions and their derivatives of the separated signals. As a result, the score function of the perceptron's outputs can be estimated directly. Simulations show good performance of the proposed non-linear BSS algorithm.

• 出版日期2014-11
• 单位江南大学