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Based on improved BP neural network of college students jump performance prediction and computer simulation

机译:基于改进BP神经网络的大学生跳跃成绩预测与计算机仿真

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Because now commonly used artificial intelligence to predict the BP neural network has some deficiency, which needs a lot of samples in practical application, and the prediction accuracy of difficulty grasping and other defects. Based on the analysis of the traditional BP algorithm particle swarm algorithm is proposed to improve the BP neural network algorithm ideas. And his application to the college students jump performance prediction, through computer simulation and actual long jump results show that the improved BP neural network algorithm accuracy is higher and relatively reasonable sample requirement. The algorithm is highly recognized by the industry.
机译:由于目前常用的人工智能来预测BP神经网络存在一定的缺陷,在实际应用中需要大量的样本,以及难于掌握的预测精度等缺陷。在分析传统BP算法的基础上,提出了粒子群算法,以改进BP神经网络算法的思想。并将其应用于大学生跳跃成绩预测中,通过计算机仿真和实际跳远结果表明,改进后的BP神经网络算法精度较高,样本要求相对合理。该算法已得到业界的高度认可。

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