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基于蚁群算法的改进ICA算法
  • ISSN号：1002-8692
  • 期刊名称：《电视技术》
  • 时间：0
  • 分类：TN911[电子电信—通信与信息系统;电子电信—信息与通信工程] TP301.6[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]空军工程大学导弹学院,陕西三原713800
  • 相关基金：基金项目：国家自然科学基金项目（60971118）
作者： 邓均明[1], 吴法文[1], 陈西宏[1], 徐宇亮[1]
关键词： 独立分量分析, FASTICA算法, 蚁群算法, independent component analysis, FastlCA algorithm, ant colony algorithm.
中文摘要：

针对FastICA算法存在依赖非线性函数选取的缺陷，为了提高分离结果的可靠性，提出一种基于蚁群算法的改进ICA算法。该算法对非线性函数没有特殊要求，以负熵近似表达式为目标函数，利用蚁群算法代替FastICA算法中的牛顿梯度法，求出最优分离矩阵B，从而对混合信号中的独立分量进行分离。仿真结果验证了改进ICA算法的有效性和优越性。

英文摘要：

The FastICA algorithm has the defect in relying on the selection of nonlinear functions. In order to improve the reliability of the separation resuits, an improved independent component analysis algorithm based on ant colony algorithm is introduced. Such algorithm has no special requirements of nonlinear function, takes the approximate expression of negative entropy as the objective function, and can be optimized by taking use of ant colony algorithm instead of Newton gradient method. The best separation matrix is found and then the independent components from the mixed signals are separated. Simulation result proves the improved independent component analysis algorithm is effective and better.

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期刊信息
  • 《电视技术》
  • 北大核心期刊（2011版）
  • 主管单位:中国电子科技集团公司
  • 主办单位:电视电声研究所
  • 主编：许盈（执行主编）
  • 地址：北京市朝阳区酒仙桥北路乙7号
  • 邮编：100015
  • 邮箱：tvea@263.net.cn; dsss@chinajournal.net.cn
  • 电话：010-59570246
  • 国际标准刊号：ISSN：1002-8692
  • 国内统一刊号：ISSN：11-2123/TN
  • 邮发代号:2-354
  • 获奖情况:
  • 第三届国家期刊奖百种重点期刊、中国期刊方阵双百...
  • 国内外数据库收录:
  • 美国剑桥科学文摘,英国科学文摘数据库,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:12712