Blind separation of binary sources with less sensors than sources
Blind separation of binary sources with less sensors than sources
复制标题
使用比源更少的传感器盲分离二进制源
DOI：
10.1109/icnn.1997.614205
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
P. Pajunen
中科院分区：
文献类型：
--
作者：
P. Pajunen
混合未知信号的盲分离是当前统计信号处理和无监督神经学习领域的一个研究热点。已经提出了几种源分离算法，其中假设至少存在与源一样多的传感器。本文提出了一种实用的算法，用于从比信源少的传感器中分离出二进制信源。该算法在自适应阶段使用约束竞争学习，通过简单地选择最佳匹配单元来实现实际分离。该算法似乎是合理的鲁棒性对小的加性噪声。
Blind separation of unknown sources from their mixtures is currently a timely research topic in statistical signal processing and unsupervised neural learning. Several source separation algorithms have been presented where it is assumed that there are at least as many sensors as sources. In this paper, a practical algorithm is proposed for separating binary sources from less sensors than sources. The algorithm uses constrained competitive learning in the adaptation phase and the actual separation is achieved by simply selecting the best matching unit. The algorithm appears to be reasonably robust against small additive noise.