说明：  独立成分分析(Independent Component Analysis，ICA)是近年来提出的非常有效的数据分析工具，它主要用来从混合数据中提取出原始的独立信号。它作为信号分离的一种有效方法而受到广泛的关注。近几年出现了一种快速ICA算法(Fast ICA)，该算法是基于定点递推算法得到的，它对任何类型的数据都适用，同时它的存在对运用ICA分析高维的数据成为可能。又称固定点(Fixed-Point)算法，是由芬兰赫尔辛基大学Hyvärinen等人提出来的。是一种快速寻优迭代算法，与普通的神经网络算法不同的是这种算法采用了批处理的方式，即在每一步迭代中有大量的样本数据参与运算。但是从分布式并行处理的观点看该算法仍可称之为是一种神经网络算法。
(Independent component analysis (ICA) is a very effective data analysis tool proposed in recent years. It is mainly used to extract the original independent signals from the mixed data. As an effective method of signal separation, it has been widely concerned. In recent years, a fast ICA algorithm (fast ICA) has appeared. The algorithm is based on the fixed-point recursive algorithm, which is applicable to any type of data. At the same time, its existence makes it possible to use ICA to analyze high-dimensional data. Also known as fixed-point algorithm, it was proposed by HYV & auml; rinen et al. It is a fast optimization iterative algorithm. Different from the common neural network algorithm, this algorithm adopts the way of batch processing, that is, there are a large number of sample data in each iteration. But from the point of view of distributed parallel processing, this algorithm can still be called a neural network algorithm.)