新型的求解非线性盲源分离的神经网络算法综述  

A Survey of Neural Network Approaches to Nonlinear Blind Source Separation

作　　者：王海林[1] 

机构地区：[1]南京邮电大学光电工程学院,江苏南京210003

出　　处：《电子质量》2008年第1期58-61,共4页Electronics Quality

摘　　要：本文回顾了一些最新的求解非线性盲源分离问题的神经网络算法。其中,对于多层感知器网络、径向基函数网络、多项式网络尤其关注。为了从非线性混叠信号中分解出唯一的源信号解,需要给神经网络加上一系列的限制。提出了三种结构限制的独立分量分析混合模型,接着讨论了加在源自交叉熵的原始代价函数上的额外的信号约束。In this paper, several recently proposed neural network approaches to nonlinear blind signal separat（on （BSS） are reviewed. Of great interest, popular muitilayer perceptron （MLP）, radial basis function （RBF） and polynomial neural networks are the focus of the paper, In order to uniquely extract the original source signals from only nonlineady mixed observalions, some forms Of constrains are always imposed on the neural networks. Three structurally constrained nonlinear independent component analysis mixing models are presented, followed by the discussion on additional signal constraints to the original cost function stemmed from the Kullback-Leibler Divergence.

关 键 词：非线性盲源分离 多层感知器网络 径向基函数网络 多项式网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]