摘要
针对粒子群算法容易过早出现早熟收敛问题,提出一种改进的PSO算法。在当前粒子陷入局部最优时,该算法根据平均粒距对部分粒子以一定的概率进行变异,从而扩大粒子群的全局搜索能力。将改进的PSO算法用来训练支持向量机,并应用在说话人识别系统中。通过实验证明改进的PSO算法在收敛速度和识别精度上都得到了改善。
Aiming at the premature convergence problem of Particle Swarm Optimization（PSO）,an improved PSO algorithm is proposed.When the current particles fall into local optimum,according to average distance of particles,this algorithm makes part of particles variate with a certain probability so that it can expand the global search ability.This improved algorithm is used to train Support Vector Machine（SVM）,then applied to speaker recognition system.The experiment shows that it can achieve higher convergence speed and higher recognition accuracy.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第33期106-108,共3页
Computer Engineering and Applications
基金
国家自然科学基金(No.609661002)~~
关键词
说话人识别
粒子群算法
支持向量机
早熟收敛
speaker recognition
particle swarm optimization
support vector machine
premature convergence
