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Research on construction and application of the GM(1,1) forecast model of Olympics track and field achievements

机译:奥运会田径成绩GM(1,1)预测模型的构建与应用研究

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Purpose - The paper attempts to establish GM(1,1) grey prediction model group for the top three Olympic track and field sports performance, and to predict the 30th London Olympic track and field results and its tendency using grey systems theory. Design/methodology/approach - Athletics sports achievements are influenced by many factors, such as the physical quality, athletes individual growth cycle, and injuring or retirement of excellent athletes, the outstanding performance of some athletes, the using of high-tech sports training instrument, the implementation plan of scientific training guidance, the introduction of advanced technology, facilities and improvement, and so on. Those aspects can make the match result uncertain, which are running in a uncertain and continually changing environment, so sports achievements have obviously grey features. Combined with grey modeling methods, and aimed at the top three Olympic track and field sports performance, this paper established GM (1,1) grey prediction model group and analysed the trend of Olympic track and field. And in the end of the paper, the 30th Olympic men's and women's the top three athletic achievements prediction intervals are also predicted. Findings - The results show that forecasting model group has high-precision. In the 46 champions prediction models, three models have the forecast accuracy of 100 percent; 27 models' forecast accuracy are greater than 99.5 percent, and the rest of the models forecast accuracy are greater than 98.58 percent. In the 46 silver medalists prediction models, five models have the forecast accuracy of 100 percent; 33 models' forecast accuracy are greater than 99.5 percent and the rest of the models' forecast accuracy is greater than 98.48 percent. In the 46 bronze medalist prediction models, four models have the forecast accuracy of 100 percent; 25 models' forecast accuracy is greater than 99.5 percent and the rest of the models forecast accuracy is greater than 98.76 percent. The essay deeply analyzes the top three achievements' trend of Olympic Games Track and field. In the end, the paper predicts the 30th Olympic track and field results. Practical implications - The method exposed in the paper can be used for the short-term or long-term prediction of sports scores metering in international competition (such as track and field, swimming, rowing, etc.), and also for personal athletic performance prediction. Originality/value - The paper succeeds in realising both grey prediction model group for the top three Olympic track and field performance in all projects, and prediction of the 30th London Olympic track and field results by using the newest developed theories: grey systems theory.
机译:目的-本文试图建立GM(1,1)灰色预测模型组用于奥林匹克运动会的前三名,并使用灰色系统理论预测第30届伦敦奥运会田径运动的成绩及其趋势。设计/方法/方法-田径运动的成就受许多因素影响,例如身体素质,运动员的个人成长周期,优秀运动员的受伤或退役,某些运动员的出色表现,高科技运动训练仪器的使用,科学培训指导的实施计划,先进技术的引进,设施的改进等。这些方面会使比赛结果不确定,因为比赛结果在不确定且不断变化的环境中运行,因此运动成绩明显具有灰色特征。结合灰色建模方法,针对奥运田径运动表现前三名,建立了GM(1,1)灰色预测模型组,并分析了奥林匹克田径运动的发展趋势。并且在本文的最后,还预测了第30届奥运会男子和女子运动成绩前三名的预测间隔。结果-结果表明,预测模型组具有较高的精度。在46个冠军预测模型中,三个模型的预测准确性为100%; 27个模型的预测准确性大于99.5%,其余模型的预测准确性大于98.58%。在46个银牌得主的预测模型中,有五个模型的预测准确度为100%; 33个模型的预测准确性大于99.5%,其余模型的预测准确性大于98.48%。在46个铜牌得主的预测模型中,有四个模型的预测准确度为100%; 25个模型的预测准确性大于99.5%,其余模型的预测准确性大于98.76%。本文深入分析了奥运会田径运动的三项成就趋势。最后,本文预测了第30届奥运会田径比赛的成绩。实际意义-本文中介绍的方法可用于短期或长期预测国际比赛中的运动成绩计量(例如田径,游泳,赛艇等),也可用于个人运动表现预测。原创性/价值-本文成功地实现了所有项目中前三名奥运会田径表现的灰色预测模型组,并通过使用最新发展的理论:灰色系统理论对第30届伦敦奥运会田径成绩进行了预测。

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