干扰识别的量子粒子群和支持向量机算法

The Quantum-behave Particle Swarm Optimization Support Vectors Machine Method of Jamming Recognition

根据支持向量机结构风险最小化原则和量子粒子群快速全局优化的特点,提出了干扰样式识别的QPSO-SVM算法。采用量子粒子群算法优化支持向量机参数,建立了干扰样式特征组分识别的模型,经过仿真试验,表明该算法具有识别率高,计算时间短的优点。

According to minimum structure risk of SVM（Support Vectors Machine） and the quickly globally optimizing ability of QPSO（Quantum behave Particle Swarm Optimization）,a novel algorithm for jamming pattern recognition based on QPSO SVM was proposed.The parameters of SVM was opti-mized by using QPSO,then the jamming pattern recognition model was constructed.Simulation results show that the algorithm can recognize jamming pattern correctly at low JSR.