基于人工神经网络的携带距离预测模型的设计与实现

J. Ko, Kyeongrok Kim, Jae-Hyun Kim
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引用次数: 0

摘要

利用高频雷达或高速摄像机测量高尔夫球速度、发射角度和旋转等参数的击球模式分析仪。但高尔夫球移动几十米的搬运距离很难测量。因此,高尔夫球的携带距离是由高尔夫球的初速度、发射角度、旋转速率等多种变量来计算的。本文基于人工神经网络(ANN)计算了高尔夫球的携带距离。人工神经网络模型使用五个因变量(球杆速度、攻角、高尔夫球速度、发射角度和旋转速率)作为输入变量。人工神经网络模型的结构由一个输入层、四个隐藏层和一个输出层组成。隐藏层的隐藏节点分别由10个、20个、20个、20个节点组成。使用均方根误差(RMSE)进行性能评估,ANN模型的RMSE为0.8。
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A Design and Implementaion of Carry Distance Prediction Model using Artificial Neural Network
A golf shot pattern analyzer, which can derive a golf ball speed, a launch angle, and a spin, measures parameters using a high frequency radar or a high speed camera. But it is difficult to measure a carry distance of golf ball moving several tens of meters. Therefore, the carry distance of golf ball is calculated by various variables such as an initial velocity of golf ball, a launch angle, a spin rate, etc. In this paper, we calculate the carry distance of golf ball based on an Artificial Neural Network (ANN). The ANN model uses five dependent variables (club speed, attack angle, golf ball speed, launch angle, and spin rate) as input variables. A structure of the ANN model consists of one input layer, four hidden layers, and one output layer. Hidden nodes of the hidden layer are composed of 10, 20, 20, and 20 nodes, respectively. A Root Mean Square Error (RMSE) is used for performance evaluation and the RMSE of the ANN model is 0.8.
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