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A Design and Implementaion of Carry Distance Prediction Model using Artificial Neural Network

机译:人工神经网络携带距离预测模型的设计与实现

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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.
机译:高尔夫击球图案分析仪可以得出高尔夫球的速度,发射角度和旋转角度,它使用高频雷达或高速摄像机测量参数。但是很难测量移动几十米的高尔夫球的携带距离。因此,高尔夫球的携带距离是通过诸如高尔夫球的初始速度,发射角度,旋转速度等各种变量来计算的。在本文中,我们基于人工神经网络来计算高尔夫球的携带距离。 (ANN)。 ANN模型使用五个因变量(球杆速度,攻角,高尔夫球速度,发射角和旋转速度)作为输入变量。 ANN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、20、20和20个节点组成。均方根误差(RMSE)用于性能评估,ANN模型的RMSE为0.8。

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