| 利用人工鱼群算法实现基于MP的信号稀疏分解* |
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| 引用本文： | 舒维杰,袁志刚,尹忠科.利用人工鱼群算法实现基于MP的信号稀疏分解*[J].计算机应用研究,2009,26(1):66-67. |
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| 作者姓名： | 舒维杰 袁志刚 尹忠科 |
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| 作者单位： | 西南交通大学,信息科学与技术学院,成都,610031 |
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| 基金项目： | 国家自然科学基金资助项目（60772084） |
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| 摘 要： | 人工鱼群算法（AFSA）是一种新的智能优化算法，具有鲁棒性强、全局收敛性好，及对初值的不敏感性等特点。将人工鱼群算法运用到信号的稀疏分解中，可快速寻找匹配追踪(MP)过程中每一步分解的最佳原子。此方法提高了信号稀疏分解的速度，算法的有效性为实验结果所证实。
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| 关 键 词： | 信号处理 稀疏分解 匹配追踪 人工鱼群算法 |
Signal MP-based sparse decomposition with artificial fish-swarm algorithm |
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| SHU Wei-jie,YUAN Zhi-gang,YIN Zhong-ke.Signal MP-based sparse decomposition with artificial fish-swarm algorithm[J].Application Research of Computers,2009,26(1):66-67. |
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| Authors: | SHU Wei-jie YUAN Zhi-gang YIN Zhong-ke |
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| Affiliation: | (School of Information Science & Technology, Southwest Jiaotong University, Chengdu 610031, China) |
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| Abstract: | Artificial fish-swarm algorithm is a new kind of intelligence optimization algorithm. It has a strong robustness and good global astringency, and it is also proved to be insensitive to initial values. This paper applied the artificial fish-swarm algorithm to signal sparse decomposition to fast search for approximately optimal atom at each step of matching pursuit. The method improves the speed of signal sparse decomposition and the validity of this algorithm is proved by experimental results. |
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| Keywords: | signal processing sparse decomposition matching pursuit (MP) artificial fish-swarm algorithm (AFSA) |
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