期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

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
下载PDF
导出
摘要 由于信号稀疏表示的优良特性,已被用于信号处理很多领域,但计算量大阻碍了它在实际中的应用。粒子群优化算法简单,易于实现,且搜索效果好。论文采用匹配追踪(Matching Pursuit, MP)算法实现信号稀疏分解,利用粒子群优化算法搜索MP过程中的最优原子。根据原子特性,优化改进后的算法。仿真结果证明了新算法的可行性。 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.
作者 钱建 赵毅智 庄智威 Jian Qian;Yizhi Zhao;Zhiwei Zhuang(School of Communication Engineering, Hangzhou Dianzi University, Hangzhou)
机构地区 杭州电子科技大学通信工程学院
出处 《计算机科学与应用》 2014年第11期282-287,共6页 Computer Science and Application
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
分类号 TP39 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献3

二级参考文献24

  • 1王东升 曹磊.混沌、分形及其应用[M].合肥:中国科学技术大学出版社,1995..
  • 2Kennedy J,et al. Particle swarm optimization. In: IEEE Int'lConf. on Neural Networks. Perth,Australia, 1995. 1942- 1948
  • 3Eberhart R,Kennedy J. A new optimizer using particle swarm theory. In: Proc. of the sixth intl. symposium on Micro Machine and Human Science, Nagoya ,Japan, 1995. 39-43
  • 4Shi Y,et al. A modified particle swarm optimizer [C]. In: IEEE World Congress on Computational Intelligence, 1998.69-73
  • 5Shi Y,Eberhart R C. Fuzzy Adaptive particle swarm optimization [C]. In: Proc. of the Congress on Evolutionary Computation,Seoul Korea, 2001
  • 6Clerc M. The swarm and the Queen: Towards a deterministic and adaptive particle swarm optimization[C]. In: Proc. of the Congress of Evolutionary Computation, 1999. 1951-1957
  • 7Lovbjerg M,Rasmussen T K, Krink T. Hybrid particle swarm optimization with breeding and subpopulations[C]. In: Proc. of the third Genetic and Evolutionary computation conf. San Francisco,USA,2001
  • 8Higasshi N, Iba H. Particle swarm optimization with Gaussian mutation [C]. In: Proc. of the Congress on Evolutionary Computation,2003. 72-79
  • 9Van den Bergh F,Engelbrecht A P. Training product unit networks using cooperative particle swarm optimizers[C]. In: Proc.of the third Genetic and Evolutionary computation conf. San Francisco ,USA, 2001
  • 10Van den Bergh F,Engelbrecht A P. Effects of swarm size cooperative particle swarm optimizers [C]. In: Proc. of the third Genetic and Evolutionary computation conf. San Francisco, USA, 2001

共引文献252

计算机科学与应用
2014年 第11期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部