基于八阶收敛牛顿迭代的Fast-ICA改进算法

摘要

The most popular solution for Blind Source Separation(BSS)problem is Independent Component Analysis (ICA), and the Fast-ICA algorithm is widely used in BSS. The traditional Fast-ICA algorithm is optimized by the qua-dratic convergence of Newton iteration method. To accelerate the convergence speed and improve the running efficiency of the algorithm, this paper gives an improved Fast-ICA algorithm with eighth-order convergence of Newton iterative method. The simulation results show that the computational speed of the improved Fast-ICA is faster than that of the tradi-tional Fast-ICA and the Fast-ICA with fifth-order convergence of Newton iteration method.%解决盲源分离问题(BSS)最常用的方法是独立分量分析方法(ICA),快速独立分量分析方法(Fast-ICA)是目前广泛使用的独立分量分析方法.传统的Fast-ICA算法利用了二阶收敛的牛顿迭代方法进行优化,为了加快算法的收敛速度,提高算法的运行效率,利用八阶收敛的牛顿迭代方法对Fast-ICA算法进行优化,通过仿真验证了基于八阶收敛的Fast-ICA算法与传统的Fast-ICA和五阶收敛的Fast-ICA算法在分离性能上基本相同,但其具有更少的迭代次数和更快的收敛速率.