BSS算法的比较

IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222) Pub Date : 2001-07-15 DOI:10.1109/IJCNN.2001.939484

Y. Singh, C. Rai

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引用次数: 3

摘要

目前已有几种基于梯度的盲源分离(BSS)算法。本文比较了三种最流行的神经算法:EASI、自然梯度和Bell-Sejnowski算法。这些算法的有效性取决于非线性激活函数。针对亚高斯和超高斯源，用不同的非线性函数对这些算法进行了评价。

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A comparison of BSS algorithms

Several gradient-based algorithms exist for performing blind source separation (BSS). In this paper we compare three most popular neural algorithms: EASI, natural gradient and Bell-Sejnowski algorithms. The effectiveness of these algorithms depends upon the nonlinear activation function. These algorithms were evaluated with different nonlinear functions for sub-Gaussian and super-Gaussian sources.

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IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)

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