一种用于盲源分离的具有横向反馈连接的线性前馈神经网络

S. Choi, A. Cichocki
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引用次数: 10

摘要

给出了非零峰度源与线性混合源的盲分离的一个新的充分必要条件。本文给出了一种新的基于奇函数(f(y)=y/sup 3/)和偶函数(f(y)=y/sup 2/)的盲分离准则,在所有源信号都具有负峰度(亚高斯)或正峰度(超高斯)的情况下,给出了理想的解。基于这一分离准则,构造了一个具有横向反馈连接的线性前馈网络。给出了理论和计算机仿真结果。
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A linear feedforward neural network with lateral feedback connections for blind source separation
We presents a new necessary and sufficient condition for the blind separation of sources having non-zero kurtosis, from their linear mixtures. It is shown here that a new blind separation criterion based on both odd (f(y)=y/sup 3/) and even (f(y)=y/sup 2/) functions, presents desirable solutions, provided that all source signals have negative kurtosis (sub-Gaussian) or have positive kurtosis (super-Gaussian). Based on this new separation criterion, a linear feedforward network with lateral feedback connections is constructed. Both theoretical and computer simulation results are presented.
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