关键词: 盲源分离（BSS）, 独立分量分析（ICA）, 快速独立分量分析（Fast-ICA）, 有理非线性函数, 切比雪夫-帕德逼近

Abstract: There are two nonlinearities（hyperbolic tangent “tanh” and Gaussian function “gauss”） in the Fast-ICA algorithm to separate super-Gaussian sources. For large-scale source signals, however, these two functions are not optimal owing to high computational cost. In order to solve this problem, this paper proposes two novel rational polynomial functions to replace the original nonlinearities. Because the rational functions can be quickly evaluated, when they are used in the Fast-ICA, the computational load of the algorithm can be effectively reduced. The simulation results show that the Fast-ICA algorithms with rational nonlinearities not only can speed up the convergence but also improve the separation performance of super-Gaussian blind source separation.

Key words: Blind Source Separation（BSS）, Independent Component Analysis（ICA）, Fast-ICA, rational nonlinearity, Tchebyshev-Pade approximant