摘要
借助遗传算法中交叉和选择的思想策略,提出一种改进的粒子群算法对模糊支持向量机的参数进行优化选择.结果表明:该方法能够减少模糊支持向量机参数选择的盲目性,增强了模糊支持向量机的泛化能力,同时也提高了其分类精度.
By means of crossover and selection strategy of genetic algorithm,an improved particle swarm optimization algorithm was proposed to optimize the parameters of fuzzy support vector machine.The results showed that this method can reduce the blindness in optimizing the parameters of fuzzy support vector machine,and can improve the generalization ability fuzzy support vector machine.Meanwhile,the classification accuracy can also be improved.
出处
《信阳师范学院学报（自然科学版）》
CAS
北大核心
2013年第2期288-291,共4页
Journal of Xinyang Normal University(Natural Science Edition)
基金
河南省科技计划项目(092300410208)
河南省教育厅科技计划项目(2008A520021)
关键词
参数优化
粒子群算法
模糊支持向量分类机
parameter optimization
particle swarm optimization
fuzzy support vector classifiers
