关键词: 调制模式识别, 信息熵, 粒子群算法, 支持向量机

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.

Key words: modulation recognition;information entropy;particle swarm optimization;support vector machine