基于自适应评价函数的独立成分分析算法

简要介绍独立成分分析（ICA）及其模型，然后在极大似然估计的框架下，基于两类参数模型--Gaussian混合密度模型和Pearson系统模型，研究了具有对称分布（包括超高斯分布与亚高斯分布）和非对称分布源混合信号的盲分离问题，给出了一种有效的基于灵活评价函数的ICA新算法，该算法在一定意义上实现了对源信号概率分布的真正全“盲”。与原有的ICA算法相比，该算法具有更广泛应用范围。模拟实验验证了算法的有效性。

Adaptive Algorithm for Independent Component Analysiswith Flexible Score Functions

After giving a brief introduction about the idea of the model of Independent Component Analysis (ICA),an algorithm for ICA without any knowledge of their probability distributions was provided.It was achieved under a maximum likelihood framework by considering Gaussian parametric density mixture model and Pearson system model.As a result,an explicit ICA algorithm with flexible score functions to various marginal densities was obtained.Simulation result shows that the proposed algorithm is able to separate a wild range of source signals,including sub-Gaussian and super-Gaussian sources,symmetric and asymmetric sources.