Training project arrangement for tennis athletes based on BP neural network model
by Wang Hao; Yuan Hong
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 9, No. 3/4, 2017

Abstract: In order to improve the prediction accuracy of athlete's tennis training effect, a kind of prediction method for athlete's tennis training effect of RBF (boundary value constraints radial basis function, BVC-RBF) neural network with boundary value constraints is proposed. Firstly, the internal and external factors that influence the athlete's tennis training effect is analysed, and the influence models of 12 indexes including quantitative load heart rate and body fat percentage are predicted and analysed emphatically; secondly, the RBF neural network algorithm with boundary value constraints is built to solve the boundary value constraint equation, so as to obtain the compensation function, and the least square method is used to train traditional RBF neural network, which achieves the improvement of prediction algorithm performance; finally, the simulation experiment shows that the proposed method provides higher prediction accuracy, which has a certain guiding value for tennis training.

Online publication date: Wed, 21-Feb-2018

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