首页> 美国卫生研究院文献>Journal of Sports Science Medicine >The Use of Neural Network Technology to Model Swimming Performance
【2h】

The Use of Neural Network Technology to Model Swimming Performance

机译:神经网络技术在游泳成绩建模中的应用

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports.Key points class="unordered" style="list-style-type:disc">The non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models.The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low.The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs.The use of neural networks for sports sciences application allowed us to create very realistic models for swimming performance prediction based on previous selected criterions that were related with the dependent variable (performance).
机译:本研究的目的是:找出能够解释年轻游泳者在200米个人混合泳和400米前爬网比赛中的表现的因素,以非线性数学方法通过人工模拟这些事件中的表现神经网络(多层感知器)和评估神经网络模型的精度来预测性能。将138名国家级别的年轻游泳运动员(65名男性和73名女性)的样本提交到一个测试电池组,该电池组包含四个不同领域:人体力学评估,旱地功能评估(强度和柔韧性),游泳功能评估(流体动力学,静水力和生物能特性) )和游泳技术评估。为了建立年轻游泳运动员的概况,开发了每种性别的主要变量与200米混合泳和400米字体爬行事件中的游泳表现之间的非线性组合。为此,使用前馈神经网络(多层感知器),在单个隐藏层中具有三个神经元。最近的证据支持了模型的预后精度(真实和估计的性能之间的误差低于0.8%)。因此,我们认为神经网络工具可以作为解决复杂问题的好方法,例如游泳和(可能)多种体育活动中的性能建模和才能识别。关键点 class =“ unordered” style =“ list-style-type:disc”> <!-list-behavior = unordered prefix-word = mark-type = disc max-label-size = 0-> 由以下原因引起的非线性分析前馈神经网络的使用使我们可以开发四个性能模型。 所构建的四个神经网络模型中的每个模型执行的真实结果与估计结果之间的平均差很小。 神经网络工具可以作为一种性能模型解析的好方法,可以替代标准统计模型,该模型假定所有输入之间定义明确的分布和独立性。 神经网络的使用体育科学应用网络使我们能够创建非常逼真的游泳体育模型基于先前选择的与因变量(性能)相关的标准进行性能预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号