以獨立分量分析為主要對象, 描述了盲信號源分離技術的基本模型，介紹了盲分離的主要方法和數學原理, 分析了盲信號源的可辨識性。提出基于神經網絡無監督學習的盲分離方法，并改進了分離效果評判指標。在生物信息處理的背景下將人工神經網絡和信息理論相結合，解決了盲信號源分離，自適應地求得分離矩陣，且可以同時分離具有正峭度和負峭度的信號源，對盲信號源分離的研究有極大的促進作用。
關 鍵 詞 盲信號源分離; 神經網絡; 無監督學習; 獨立分量分析板

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.