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一种改进的自适应不完整自然梯度盲源分离算法

, PP. 667-673

Keywords: 盲源分离,不完整约束,自然梯度,激活函数

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Abstract:

与自然梯度盲源分离算法相比,不完整自然梯度算法避免因源信号非平稳或幅值快速变化而引起的数值不稳定.在深入分析和推导该算法的基础上,针对其中非线性激活函数难以确定的困难,提出一种利用峰度对激活函数进行自适应选择的改进算法.该算法无需已知源信号的先验信息,既保留了不完整自然梯度算法恢复非平稳源信号的优势,又可使其适用于服从任意分布的源信号.仿真比较结果表明,该方法性能优于选择正切函数作为激活函数的不完整自然梯度算法,分离效果较好.

References

[1]  Zhang L Q, Amari S, Cichocki A. Natural Gradient Approach to Blind Separation of Over- and Under-Complete Mixtures // Proc of the International Workshop on Independent Component Analysis and Blind Signal Separation. Aussois, France, 1999: 455-460
[2]  Amari S. Natural Gradient Learning for Over- and Under-Complete Bases in ICA. Neural Computation, 1999, 11(8): 1875-1883
[3]  Amari S, Chen T P, Cichocki A. Non-Holonomic Constraints in Learning Algorithms for Blind Source Separation. Neural Computation, 2000, 12(7): 1463-1484
[4]  Cichocki A, Unbehauen R, Moszczyński L, et al. A New On-Line Adaptive Learning Algorithm for Blind Separation of Sources // Proc of the International Joint Conference on Neural Networks. Taiwan, China, 1994: 406-411
[5]  Amari S. Natural Gradient Works Efficiently in Learning. Neural Computation, 1998, 10(2): 251-276
[6]  Bell A J, Sejnowski T J. An Information Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation, 1995, 7(6): 1129-1159
[7]  Yang H H, Amari S. A Stochastic Natural Gradient Descent Algorithm for Blind Signal Separation // Proc of the IEEE Workshop on Neural Networks for Signal Processing. Kyoto, Japan, 1997: 436-444
[8]  Comon P. Independent Component Analysis, A New Concept? Signal Processing, 1994, 36(3): 287-314
[9]  Cichocki A, Amari S. Adaptive Blind Signal and Image Processing: Learning Algorithm and Applications. Chichester, UK: John Wiley, 2002

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