基于样条插值与人工蜂群优化的非线性盲源分离算法

陈雷; 甘士忠; 张立毅; 王光艳

doi:10.11959/j.issn.1000-436x.2017147

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基于样条插值与人工蜂群优化的非线性盲源分离算法

学术论文 | 更新时间：2024-06-05

- 基于样条插值与人工蜂群优化的非线性盲源分离算法
- Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization
- 通信学报 2017年38卷第7期页码：36-46
- 作者机构：
  
  1. 天津商业大学信息工程学院，天津 300134
  2. 天津工业大学电子信息工程学院，天津 300387
- 作者简介：
  
  [ "陈雷（1980-），男，河北唐山人，博士后，天津商业大学副教授，主要研究方向为盲信号处理、仿生智能计算等。" ]
  [ "甘士忠(1994-)，男，河南信阳人，天津工业大学硕士生，主要研究方向为盲信号处理、仿生智能计算等。" ]
  [ "张立毅（1963-），男，山西忻州人，博士，天津商业大学教授、博士生导师，主要研究方向为盲信号处理、信号检测与处理等。" ]
  [ "王光艳（1975-），女，河北邯郸人，博士，天津商业大学副教授，主要研究方向为盲信号处理、语音增强、水下语音通信等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61401307);中国博士后科学基金资助项目(2014M561184);天津应用基础与前沿技术研究计划基金资助项目(15JCYBJC17100);天津市应用基础与前沿技术研究计划基金资助项目(14JCZDJC32600);天津市科技特派员基金资助项目(16JCTPJC48400)
- DOI：10.11959/j.issn.1000-436x.2017147
  中图分类号： TN911.7
- 网络出版日期：2017-07，
  
  纸质出版日期：2017-07-25
- 稿件说明：
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陈雷, 甘士忠, 张立毅, 等. 基于样条插值与人工蜂群优化的非线性盲源分离算法[J]. 通信学报, 2017,38(7):36-46.

Lei CHEN, Shi-zhong GAN, Li-yi ZHANG, et al. Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization[J]. Journal on communications, 2017, 38(7): 36-46.
陈雷, 甘士忠, 张立毅, 等. 基于样条插值与人工蜂群优化的非线性盲源分离算法[J]. 通信学报, 2017,38(7):36-46. DOI： 10.11959/j.issn.1000-436x.2017147.

Lei CHEN, Shi-zhong GAN, Li-yi ZHANG, et al. Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization[J]. Journal on communications, 2017, 38(7): 36-46. DOI： 10.11959/j.issn.1000-436x.2017147.

摘要

针对更加复杂的非线性混合情况，提出一种基于样条插值拟合与群智能优化的后非线性盲源分离算法。采用样条插值函数拟合去非线性函数，使用负熵作为分离的评价准则，建立分离模型。分离过程采用改进的人工蜂群算法优化求解样条插值节点参数，并在分离的目标函数中引入相关性约束条件进行解空间范围限制，克服分离过程中存在的异常值现象。针对语音数据的分离实验结果表明，所提算法能够有效实现非线性混合信号的盲分离，较传统的基于奇数多项式拟合的分离算法具有更高的分离精度。

Abstract

A post-nonlinear blind source separation algorithm based on spline interpolation fitting and artificial bee colony optimization was proposed for the more complicated nonlinear mixture situations.The separation model was constructed by using the spline interpolation to fit the inverse nonlinear distortion function and using entropy as the separation criterion.The spline interpolation node parameters were solved by the modified artificial bee colony optimization algorithm.The correlation constraint was added into the objective function for limiting the solution space and the outliers wuld be restricted in the separation process.The results of speech sounds separation experiment show that the proposed algorithm can effectively realize the signal separation for the nonlinear mixture.Compared with the traditional separation algorithm based on odd polynomial fitting

the proposed algorithm has higher separation accuracy.

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