基于支持向量机的自行车运动员综合素质评价体系研究  

Research on Comprehensive Quality Evaluation System of Cyclist Based on Support Vector Machine

作　　者：杨琳[1] 李媛 杨艳[1] 韩婧 韩美林 乔成芳[1] YANG Lin;LI Yuan;YANH Yan;HAN Jing;HAN Meilin;QIAO Chengfang(Shangluo University,Shangluo 726000;Sports Bureau of Shangluo City,Shangluo 726000)

机构地区：[1]商洛学院,商洛726000 [2]商洛市体育局,商洛726000

出　　处：《计算机与数字工程》2019年第4期838-843,948,共7页Computer & Digital Engineering

基　　金：国家自然基金项目(编号:21703135);国家级大学生创新创业训练项目(编号:201811396016);陕西省体育局科研常规课题(编号:17056);陕西省2018年大学生创新创业训练计划项目(编号:2980);商洛学院2018年根植地方行动计划立项项目(编号:gz201831);教育部高等教育司产学合作协同育人项目(编号:201702071215;201802153211)资助

摘　　要：针对自行车运动员综合素质评价结果和影响因素之间的非线性关系问题,建立一套基于SVM算法的自行车运动员综合素质评价体系。选取225个运动员的身体形态、身体机能、运动能力、专项能力4类指标中的16个影响因素作为算法三种方法来优化网络模型的参数。结果表明,改进的GA别准确率较高,可以达到98.2906%,可为自行车运动员的综合素质评价提供科学的方法。A comprehensive quality evaluation system of cyclists based on SVM algorithm is introduced in order to solve the problem that the nonlinear relationship between the comprehensive quality and the factors of the cyclists.The 16 factors of the 4 indexes of body shape,body function,sports ability and special ability of the 225 cyclists are chosen as the research objects.The parameters of the network model are optimized by three methods,the grid search method,the GA algorithm and the improved GA algorithm.The results show that the improved GA algorithm has a high recognition accuracy,and it can reach 98.2906%.It can provide a scientific method for evaluating the comprehensive quality of cyclists.

关 键 词：支持向量机 遗传算法 自行车运动员 综合素质 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]