基于无监督学习的盲信号源分离技术研究

以独立分量分析为主要对象, 描述了盲信号源分离技术的基本模型，介绍了盲分离的主要方法和数学原理, 分析了盲信号源的可辨识性。提出基于神经网络无监督学习的盲分离方法，并改进了分离效果评判指标。在生物信息处理的背景下将人工神经网络和信息理论相结合，解决了盲信号源分离，自适应地求得分离矩阵，且可以同时分离具有正峭度和负峭度的信号源，对盲信号源分离的研究有极大的促进作用。
关 键 词 盲信号源分离; 神经网络; 无监督学习; 独立分量分析板

This paper focuses on the independent component analysis presenting a review on the basic model, the main method, the mathematical principle of blind source separation，analyzing the possibility of separation. The paper puts forward the method that based on the neural network unsupervised learning, also, improves the index on separation effects. Under the biology information processing background, combining artificial neural network with information theory to resolve this kind of problem can get the separation matrix adaptively by itself. It can separate mixtures which have both positive and negative kurtosis, and promotes the research of blind source separation greatly.