Adaptive on-line algorithm based on independent component analysis
基于独立分量分析的自适应在线算法

ICA is an efficient signal processing method which arose in recent years, an important problem learning in adaptive ICA is opting learning step. According to variable step thinking, this paper defined similarity measure which described the state of signal separation, to measure the level of similarity between output components, and thus developed an improved adaptive line algorithm. Adjusting the learning step on the basis of traditions of degree of signal separation which was reflected by dependent measure, and established the nonlinear relation between learning step and similarity measure variation, and overcame the disadvantages of traditional algorithms in the channel variation circumstances in the process of adaptive step. Performance analysis and simulation results show that separative signal has better performance in convergence and steady.