| 改进微分进化算法在压缩感知中的应用 |
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| 引用本文： | 闵涛,李艳敏. 改进微分进化算法在压缩感知中的应用[J]. 计算机系统应用, 2018, 27(6): 124-128 |
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| 作者姓名： | 闵涛 李艳敏 |
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| 作者单位： | 西安理工大学 理学院, 西安 710054,西安理工大学 理学院, 西安 710054 |
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| 基金项目： | 国家自然科学基金（51679186）；青年科学基金（11601418） |
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| 摘 要： | 压缩感知是基于信号稀疏性提出的采样理论，它在压缩成像、医学图像、雷达成像、天文学、通信等领域都有广泛的应用.压缩感知问题的求解本质上是一个优化问题，本文在微分进化算法的基础上对其改进，提出了一种改进微分进化算法，将其应用于压缩感知问题的求解中，取得了良好的效果.
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| 关 键 词： | 微分进化算法 改进微分进化算法 压缩感知 |
| 收稿时间： | 2017-09-26 |
| 修稿时间： | 2017-10-18 |
Application of Improved Differential Evolution Algorithm in Compressed Sensing |
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| MIN Tao and LI Yan-Min. Application of Improved Differential Evolution Algorithm in Compressed Sensing[J]. Computer Systems& Applications, 2018, 27(6): 124-128 |
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| Authors: | MIN Tao and LI Yan-Min |
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| Affiliation: | School of Science, Xi''an University of Technology, Xi''an 710054, China and School of Science, Xi''an University of Technology, Xi''an 710054, China |
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| Abstract: | Compressed sensing is a sampling theory based on the sparsity of the signal. It has been widely used in the fields of compression imaging, medical imaging, radar imaging, astronomy, communication, and so on. The solution of the compressed sensing problem is essentially an optimization problem, on the basis of differential evolution algorithm. This study proposed an improved differential evolution algorithm, and the algorithm is applied to the solution of compressed sensing problem, and has achieved sound results. |
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| Keywords: | differential evolution algorithm improved differential evolution algorithm compressed sensing |
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