摘要
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。
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 quadratic 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 traditional Fast-ICA and the Fast-ICA with fifth-order convergence of Newton iteration method.
作者
陈梦
何选森
CHEN Meng;HE Xuansen(College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China)
出处
《计算机工程与应用》
CSCD
北大核心
2017年第11期178-181,251,共5页
Computer Engineering and Applications
基金
湖南省高校创新平台开放基金项目(No.14K022)
