Authors: Wang Hao; Yuan Hong
Addresses: Sports Department, Northeast Petroleum University, Daqing, Heilongjiang, 163318, China ' Sports Department, Northeast Petroleum University, Daqing, Heilongjiang, 163318, China
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.
Keywords: tennis training; boundary value constraint; RBF neural network; least squares; prediction accuracy.
International Journal of Reasoning-based Intelligent Systems, 2017 Vol.9 No.3/4, pp.144 - 148
Available online: 21 Feb 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article