计算机工程与科学2013，Vol.35Issue(7)：102-107,6.DOI:10.3969/j.issn.1007-130X.2013.07.017
改进粒子群算法在调制模式识别中的应用
Improved particle swarm optimization algorithm and its application in modulation recognition
秦立龙 1王振宇2
作者信息
- 1. 国防科学技术大学电子科学与工程学院,湖南长沙410073
- 2. 解放军电子工程学院,安徽合肥230037
- 折叠
摘要
Abstract
In order to resolve the problems that the standard PSO algorithm is apt to be easily trapped in local optima and the LDW-PSO algorithm cannot adapt to the complex and nonlinear optimization,the paper proposes a modified particle swarm optimization algorithm based on the information entropy theory,named EPSO.The information entropy value is used by EPSO to determine the inertia weights,which make the algorithm have the ability of "explore" and "exploit" adaptively.The new algorithm is realized for the parameter selection of support vector machine.The simulation results prove that the proposed EPSO is stable.Compared with PSO and LDW-PSO,EPSO enhances the ability of escaping from local optimal solution,and becomes more feasible in engineering application.关键词
调制模式识别/信息熵/粒子群算法/支持向量机Key words
modulation recognition / information entropy/ particle swarm optimization / support vector machine分类
信息技术与安全科学引用本文复制引用
秦立龙,王振宇..改进粒子群算法在调制模式识别中的应用[J].计算机工程与科学,2013,35(7):102-107,6.
