基于粒子群优化和原子特性的匹配追踪算法
Matching Pursuit Based on PSO and Atomic Property
作者: 钱 建 , 赵毅智 , 庄智威 ：杭州电子科技大学通信工程学院，杭州;
关键词: 稀疏表示； 计算量； 粒子群优化； 原子特性； Sparse Representation； Computation； Particle Swarm Optimization； Atomic Property
摘要:Abstract: As sparse representation of signals has excellent characteristics, it has been applied in several fields of signal processing. But it has a large scale of computing, which hinders its application in practical signal processing. Particle swarm optimization is simple to be realized, and the searching result is good. In this paper, Matching Pursuit is used to realize sparse representation of signals, and particle swarm optimization is used to effectively search the best atom in the process of MP. According to the property of atoms, the improved algorithm is optimized. At last, the simulation results demonstrate the feasibility of the new algorithm.
文章引用: 钱 建 , 赵毅智 , 庄智威 (2014) 基于粒子群优化和原子特性的匹配追踪算法。 计算机科学与应用， 4， 282-287. doi: 10.12677/CSA.2014.411039
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