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引用次数: 0

摘要

几乎所有读者都应该知道,光屏障通常用于测量各种物体的速度。这些装置使用方便,坚固耐用,成本低。尽管具有优势,但当感兴趣的物体在多个空间维度上移动时,光屏障表现出一定的局限性。本文讨论了一种物理设置,其中光屏障也可以用于二维轨迹的情况下。然而,这种设置需要相当复杂的计算。因此,本文通过不同的神经网络模型来进行这些计算。结果表明,反向传播网络和径向基函数能够实现小于0.21%的残差,这对于大多数运动和日常应用来说已经足够了。
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Surface Light Barriers
It should be known to almost all readers that light barriers are commonly used for measuring the speed of various objects. These devices are easy to use, quite robust, and of low cost. Despite their advantages, light barriers exhibit certain limitations that occur when the objects of interest move in more than one spatial dimension. This paper discusses a physical setup in which light barriers can also be used in case of two-dimensional trajectories. However, this setup requires rather complicated calculations. Therefore, this paper performs these calculations by means of different neural network models. The results show that backpropagation networks as well as radial basis functions are able to achieve a residual error less than 0.21 %, which is more than sufficient for most sports and everyday applications.
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