基于量子进化的信号稀疏分解方法

量子电子学报 ›› 2019, Vol. 36 ›› Issue (4): 393-401.

基于量子进化的信号稀疏分解方法

余发军1*，瞿博阳1，刘义才2

1中原工学院电子信息学院，河南郑州 450007； 2武汉商学院机电工程与汽车服务学院，湖北武汉 430056

收稿日期:2018-09-13 修回日期:2018-10-23 出版日期:2019-07-28 发布日期:2019-07-11
作者简介:余发军（1981-），男，河南商城，博士，副教授，主要从事智能计算与信息处理方面的研究。E-mail：524663102@qq.com
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金, 61673404，61473266),Key scientific research projects in Colleges and universities in Henan of China (河南省高校重点科研项目, 18B510020)

Signal sparse decomposition method based on quantum evolutionary algorithm

YU Fajun1*, QU Boyang1, LIU Yicai2

1 School of Electric and Information Engineer, Zhongyuan University of Technology, Zhengzhou 450007, China; 2 School of Mechanical-electronic and Automobile Engineering, Wuhan Business University, Wuhan 430056, China

Received:2018-09-13 Revised:2018-10-23 Published:2019-07-28 Online:2019-07-11

摘要/Abstract

摘要： 稀疏分解将信号表达为冗余字典中少量原子的线性组合，其分解的精度对其广泛应用具有重要影响。提出的基于量子进化算法的稀疏分解方法，利用增强型量子比特概率幅对Gabor原子进行染色体编码，采用简化形式的梯度进化操作和逐代缩减的变异操作进行种群个体的更新，以稀疏分解的残余信号与Gabor原子的内积作为适应度函数，筛选出每次稀疏分解的最佳原子。通过两个仿真信号的稀疏分解实验和轴承振动信号的故障特征提取实验，验证了所提方法较其他方法具有更高的分解精度。

关键词: 量子光学, 量子进化算法, 逐代缩减变异操作, 信号稀疏分解

Abstract: Sparse decomposition represents a signal as a linear combination of a small number of atoms in a redundant dictionary. The accuracy of its decomposition has an important influence on its wide application.A sparse decomposition method based on quantum evolution algorithm is proposed. The Gabor atoms are encoded by the enhanced qubit probability amplitude. The simplified form of gradient evolution operation and generation by generation reduction mutation operation are used to update the individual population. And the inner products of the residual signal of sparse decomposition and Gabor atoms are used as the fitness to filter out the best atoms for each sparse decomposition. Two experiments of sparse decomposition of simulation signals and the fault feature extraction experiments of the bearing vibration signals are carried out. The results are proved that the proposed method has a higher resolution than the other methods.

Key words: quantum optics, quantum evolutionary algorithm, generation by generation reduction mutation operation, signal sparse decomposition

余发军1*，瞿博阳1，刘义才2. 基于量子进化的信号稀疏分解方法[J]. 量子电子学报, 2019, 36(4): 393-401.

YU Fajun1*, QU Boyang1, LIU Yicai2. Signal sparse decomposition method based on quantum evolutionary algorithm[J]. Chinese Journal of Quantum Electronics, 2019, 36(4): 393-401.

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