| 夏 奎,李 炜,邱意敏,王 晶.基于改进型象群优化算法的 BSS 方法[J].电子测量与仪器学报,2021,35(10):153-160 |
| 基于改进型象群优化算法的 BSS 方法 |
| BSS method based on improved elephant herding optimization algorithm |
| |
| DOI： |
| 中文关键词: 盲源分离 象群优化 邻域搜索 量子粒子群优化 |
| 英文关键词:blind source separation elephant swarm optimization neighborhood search quantum-behaved particle swarm optimization |
| 基金项目:安徽工程大学检测技术与节能装置安徽省重点实验室开放研究基金资助项目(2017070503B026 A04)、安徽省省级质量工程"六卓越、一拔尖"卓越人才培养创新项目(2020zyrc039)、安徽省省级质量工程教学研究一般项目(2020jyxm1371)、安徽工程大学校级本科教学质量提升计划研究项目(2020jyxm16)、安徽高校自然科学研究项目(KJ2020A0351)、安徽高校自然科学研究项目(KJ2020A0345)、安徽工程大学科研项目(Xjky2020031)资助 |
|
|
| Author | Institution |
| Xia Kui | 1. Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University,2. Key Laboratory of Advanced Perception and Intelligent Control for High-end Equipment,
Ministry of Education, Anhui Polytechnic University |
| Li Wei | 1. Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University,2. Key Laboratory of Advanced Perception and Intelligent Control for High-end Equipment,
Ministry of Education, Anhui Polytechnic University |
| Qiu Yimin | 1. Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University,2. Key Laboratory of Advanced Perception and Intelligent Control for High-end Equipment,
Ministry of Education, Anhui Polytechnic University |
| Wang Jing | 1. Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University,2. Key Laboratory of Advanced Perception and Intelligent Control for High-end Equipment,
Ministry of Education, Anhui Polytechnic University |
|
| 摘要点击次数: 389 |
| 全文下载次数: 533 |
| 中文摘要: |
| 为了克服传统群智能算法在求解盲源分离(BSS)问题时收敛速度慢和分离精度差的缺点,提出一种基于改进型象群优
化(IEHO)算法的 BSS 方法。 该方法利用独立性原则,融合分离信号的峭度和互信息来构建目标函数。 在氏族更新阶段,通过
改进算法比例因子并加入邻域搜索,提高了算法搜索方式的多样性;在分离阶段,引入量子粒子群优化策略,提高了算法的全局
搜索能力。 仿真结果表明,与传统的象群优化算法和粒子群优化算法相比,IEHO 算法的寻优效果较好,并成功实现了图像信号
和语音信号的盲源分离,分离精度更高,收敛速度更快。 |
| 英文摘要: |
| In order to overcome the disadvantages of slow convergence speed and poor separation accuracy when solving blind source
separation (BSS) problems in traditional swarm intelligence algorithms, a BSS method based on improved elephant herding optimization
(IEHO) is proposed. The method uses the principle of independence to construct the objective function, combining the kurtosis and the
mutual information of separating signals. In the clan update stage, the scale factor of algorithm is modified as well as exploiting the
neighborhood search to improve the diversity of the search. In the separation stage, the quantum-behaved particle swarm optimization
strategy is introduced to improve the global search ability of the algorithm. The simulation results show that the IEHO algorithm has a
better optimization effect compared with the traditional elephant herding optimization algorithm and the particle swarm optimization
algorithm. Meanwhile, the proposed algorithm can also realize blind source separation of images and speech successfully, with higher
separation accuracy and faster convergence speed. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
