基于支持向量机的自行车运动员综合素质评价体系研究
|
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.
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.
引文
[1]冯连世,冯美云,冯炜权.优秀运动员身体机能评定方法[M].北京:人民体育出版社,2003:111-112.FENG Lianshi,FENG Meiyun,FENG Weiquan.Physical Function Evaluation Method for Elite Athletes[M].Beijing:People's Sports Publishing House of China of China,2003:111-112.
[2]李之俊,马国强,苟波.自行车运动员专项体能研究进展[J].上海:体育科研,2005,26(5):53-58.LI Zhijun,MA Guoqiang,GOU Bo.Research progress on specific physical fitness of cyclists[J].Shanghai:Sport Science Research,2005,26(5):53-58.
[3]刘献武.运动选材学[M].北京:人民体育出版社,1991:32-63.LIU Xianwu.Sports Material Selection[M].Beijing:People's Sports Publishing House of China,1991:32-63.
[4]曾凡辉,王路德,邢文华,等.运动员科学选材[M].北京:人民体育出版社,1992:77-91.ZENG Fanhui,WANG Lude,XING Wenhua,et al.Scientific selection of athletes[M].Beijing:People's Sports Publishing House of China,1992:77-91.
[5]钟添发,田麦久,王路德.运动员竞技能力模型与选材标准[M].北京:人民体育出版社,1994:4-23.ZHONG Tianfa,TIAN Maijiu,WANG Lude.Athletes‘Competitive Ability Model and Selection Standard[M].Beijing:People’s Sports Publishing House of China,1994,4-23.
[6]刘元田.自行车运动员选材指标查询与评价软件的初步研制[D].北京:北京体育大学,2003:16-19.LIU Yuantan.Preliminary development of the software for inquiry and evaluation of the selection index of cyclists[D].Beijing:Beijing Sports University,2003:16-19.
[7]平渊.青少年与优秀男子自行车运动员身体形态特征及专项体能的比较研究[D].苏州:苏州大学,2014:17-21.PING Yuan.A comparative study on the physical characteristics and specific physical fitness of young and elite male cyclists[D].Suzhou:Soochow University,2014:17-21.
[8]崔建光.对自行车运动员大强度训练期某些生化指标的监测研究[D].济南:山东师范大学,2008.CUI Jianguang.Monitoring and Research on some biochemical indexes of cyclists during intensive training[D].Jinan:Shandong Normal University,2008.
[9]曹永峰,赵燕君.基于GA-BP神经网络的计算机智能化图像识别技术探究[J].应用激光,2017,37(01):139-143.CAO Yongfeng,ZHAO Yanjun.Computer Intelligent Image Recognition Technology Based on GA-BP Neural Network[J].Applied Laser,2017,37(01):139-143.
[10]Srivastava,N.,Hinton,G.,Krizhevsky,A.,Sutskever,I.&Salakhutdinov,R.Dropout:a simple way to prevent neural networks from overfitting[J].Machine Learning,2014,15:1929-1958.
[11]何静,刘林凡,张昌凡,等.参数优化的支持向量机机车车轮状态检测[J].电子测量与仪器学报,2016,30(11):1709-1717.HE Jing,LIU Linfan,ZHANG Changfan,et al.Parameter optimization for wheel condition detection of support vector machine locomotives[J].Journal of Electronic Measurement and Instruments,2016,30(11):1709-1717.
[12]戴远航,陈磊,张玮灵,等.基于多支持向量机综合的电力系统暂态稳定评估[J].中国电机工程学报,2016,36(05):1173-1180.DAI Yuanhang,CHEN Lei,ZHANG Weiling,et al.Transient stability assessment of power system based on multi-support vector machines[J].Proceedings of the CSEE,2016,36(05):1173-1180.
[13]刘浩然,赵翠香,李轩,等.一种基于改进遗传算法的神经网络优化算法研究[J].仪器仪表学报,2016,37(07):1573-1580.LIU Haoran,ZHAO Cuixiang,LI Xuan,et al.A study of neural network optimization algorithm based on improved genetic algorithm[J].Chinese Journal of Scientific Instrument,2016,37(07):1573-1580.
[14]Y LeCun,Y Bengio,G Hinton.Deep Learning[J].Nature,2015,521:436-444.
[2]李之俊,马国强,苟波.自行车运动员专项体能研究进展[J].上海:体育科研,2005,26(5):53-58.LI Zhijun,MA Guoqiang,GOU Bo.Research progress on specific physical fitness of cyclists[J].Shanghai:Sport Science Research,2005,26(5):53-58.
[3]刘献武.运动选材学[M].北京:人民体育出版社,1991:32-63.LIU Xianwu.Sports Material Selection[M].Beijing:People's Sports Publishing House of China,1991:32-63.
[4]曾凡辉,王路德,邢文华,等.运动员科学选材[M].北京:人民体育出版社,1992:77-91.ZENG Fanhui,WANG Lude,XING Wenhua,et al.Scientific selection of athletes[M].Beijing:People's Sports Publishing House of China,1992:77-91.
[5]钟添发,田麦久,王路德.运动员竞技能力模型与选材标准[M].北京:人民体育出版社,1994:4-23.ZHONG Tianfa,TIAN Maijiu,WANG Lude.Athletes‘Competitive Ability Model and Selection Standard[M].Beijing:People’s Sports Publishing House of China,1994,4-23.
[6]刘元田.自行车运动员选材指标查询与评价软件的初步研制[D].北京:北京体育大学,2003:16-19.LIU Yuantan.Preliminary development of the software for inquiry and evaluation of the selection index of cyclists[D].Beijing:Beijing Sports University,2003:16-19.
[7]平渊.青少年与优秀男子自行车运动员身体形态特征及专项体能的比较研究[D].苏州:苏州大学,2014:17-21.PING Yuan.A comparative study on the physical characteristics and specific physical fitness of young and elite male cyclists[D].Suzhou:Soochow University,2014:17-21.
[8]崔建光.对自行车运动员大强度训练期某些生化指标的监测研究[D].济南:山东师范大学,2008.CUI Jianguang.Monitoring and Research on some biochemical indexes of cyclists during intensive training[D].Jinan:Shandong Normal University,2008.
[9]曹永峰,赵燕君.基于GA-BP神经网络的计算机智能化图像识别技术探究[J].应用激光,2017,37(01):139-143.CAO Yongfeng,ZHAO Yanjun.Computer Intelligent Image Recognition Technology Based on GA-BP Neural Network[J].Applied Laser,2017,37(01):139-143.
[10]Srivastava,N.,Hinton,G.,Krizhevsky,A.,Sutskever,I.&Salakhutdinov,R.Dropout:a simple way to prevent neural networks from overfitting[J].Machine Learning,2014,15:1929-1958.
[11]何静,刘林凡,张昌凡,等.参数优化的支持向量机机车车轮状态检测[J].电子测量与仪器学报,2016,30(11):1709-1717.HE Jing,LIU Linfan,ZHANG Changfan,et al.Parameter optimization for wheel condition detection of support vector machine locomotives[J].Journal of Electronic Measurement and Instruments,2016,30(11):1709-1717.
[12]戴远航,陈磊,张玮灵,等.基于多支持向量机综合的电力系统暂态稳定评估[J].中国电机工程学报,2016,36(05):1173-1180.DAI Yuanhang,CHEN Lei,ZHANG Weiling,et al.Transient stability assessment of power system based on multi-support vector machines[J].Proceedings of the CSEE,2016,36(05):1173-1180.
[13]刘浩然,赵翠香,李轩,等.一种基于改进遗传算法的神经网络优化算法研究[J].仪器仪表学报,2016,37(07):1573-1580.LIU Haoran,ZHAO Cuixiang,LI Xuan,et al.A study of neural network optimization algorithm based on improved genetic algorithm[J].Chinese Journal of Scientific Instrument,2016,37(07):1573-1580.
[14]Y LeCun,Y Bengio,G Hinton.Deep Learning[J].Nature,2015,521:436-444.