Sports Culture Research Base, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China.
Comput Intell Neurosci. 2021 Jul 30;2021:6819493. doi: 10.1155/2021/6819493. eCollection 2021.
This study constructs a new radial basis function-particle swarm optimization neural network (RBFNN-PSO) system, which is applied to the evaluation system of physical education teaching effect. In order to verify the evaluation performance of the RBFNN-PSO system, the traditional RBF neural network system is used as the control, and the training is carried out. The results show that the RBFNN-PSO system can reach the convergence value faster than the traditional RBF neural network system in the training, and the training error is smaller. The results show that the scoring error of RBFNN-PSO system is smaller than that of RBF neural network system, with higher accuracy and smaller error. The experimental results show that the RBFNN-PSO is superior to the traditional RBF neural network in error and accuracy.
本研究构建了一种新的径向基函数-粒子群优化神经网络（RBFNN-PSO）系统，并将其应用于体育教学效果评价系统中。为了验证 RBFNN-PSO 系统的评价性能，采用传统的 RBF 神经网络系统作为控制，并进行了训练。结果表明，在训练过程中，RBFNN-PSO 系统比传统的 RBF 神经网络系统更快地达到收敛值，并且训练误差更小。结果表明，RBFNN-PSO 系统的评分误差小于 RBF 神经网络系统，具有更高的准确性和更小的误差。实验结果表明，RBFNN-PSO 在误差和准确性方面优于传统的 RBF 神经网络。
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