摘要
针对现有盲源分离算法性能受限于非线性函数选择且算法实现复杂、计算量大的问题,文章提出了一种基于人工免疫算法的盲信号分离算法,达到满足实际应用中有效分离的需求。该算法不依赖于源信号概率密度的非线性函数估计,通过免疫算法最小化信号的互信息,实现对观测混合信号的分离。基于免疫算法的盲信号分离,利用免疫算法隐形并行处理,具有较好的全局搜索性能和易收敛到最优解的特点。仿真分析表明,与传统的ICA盲分离算法相比,所提出的算法对于多路混叠信号具有更好的分离效果。
In view of the confined performance and computational complexity problems of the existing blind source separation algorithms due to the choice of nonlinear functions,this paper proposes a blind source separation algorithm based on artificial immune algorithm for the purpose of the effective separation in practical application.The proposed algorithm does not depend on the estimation of probability density properties of the source signals,but implements mutual information minimum via immune algorithm to realize the blind separation of source signals.This algorithm utilizes immune algorithm's invisible parallel processing,and has the character of strong global search performance and easy to converge to the optimal solution.Simulation experiments and analysis show that the proposed algorithm has better separation performance for multi-channel mixed signals.
作者
何庆
骆忠强
He Qing;Luo Zhongqiang(Faculty of Eelectronical Engineering, Luzhou Vocational and Technical College, Luzhou, Sichuan 646000, China;Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering)
出处
《计算机时代》
2018年第3期42-45,49,共5页
Computer Era
基金
人工智能四川省重点实验室开放基金项目(2017RZJ01)
企业信息化与物联网测控技术四川省高校重点实验室开放基金项目(2017WZJ01)
四川理工学院人才引进项目(2017RCL11)
关键词
盲源分离
免疫算法
互信息
独立分量分析
blind source separation
immune algorithm
mutual information
independent component analysis
