Construction of special physical training measurement index system based on artificial intelligence and neural network

The concept of “Internet+Sports” has promoted the application of artificial intelligence and other emerging technologies in the field of sports. This paper mainly focuses on the special physical training, and explores the application and realization path of artificial intelligence technology in physical training test. In this paper, PSO-BP model is constructed based on BP neural network optimized by PSO intelligent algorithm and applied in physical training test. In addition, for the classification of physical training, this paper follows the basic principles of physical training system construction, establishes the physical training measurement index system through the results of expert solicitation, and determines the weights of each index by using the hierarchical analysis method. Through the empirical analysis of the PSO-BP model in this paper, it can be seen that the fitting results of the training samples of male and female students show that the corresponding correlation coefficients of male and female students are 0.99908 and 0.99898, respectively.The errors of the evaluation output values of the physical training measurements and the expected values are within ±3.5, and the prediction error of the BP neural network model optimized by the PSO algorithm is significantly reduced, and the relative errors of the evaluation of male and female students are reduced by 0.988% and 0.833%, respectively. The results show that the results of physical training measurement and evaluation using PSO-BP neural network model are more accurate, which proves that the performance of PSO-BP neural network in this paper has been effectively improved and optimized, and at the same time, it can meet the application requirements of physical training measurement and evaluation.

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Publication Details

Journal
Journal of Combinatorial Mathematics and Combinatorial Computing
Published
2025-04-15
DOI
https://doi.org/10.61091/jcmcc127a-147
Primary Topic
E-commerce and Technology Innovations
Type
article
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article

Construction of special physical training measurement index system based on artificial intelligence and neural network

Xiaoping Yang, Jian-Hua Bu, Fuqiang Wang
Journal of Combinatorial Mathematics and Combinatorial Computing
E-commerce and Technology Innovations
article

Construction of special physical training measurement index system based on artificial intelligence and neural network

Xiaoping Yang, Jian-Hua Bu, Fuqiang Wang
article en

Abstract

The concept of “Internet+Sports” has promoted the application of artificial intelligence and other emerging technologies in the field of sports. This paper mainly focuses on the special physical training, and explores the application and realization path of artificial intelligence technology in physical training test. In this paper, PSO-BP model is constructed based on BP neural network optimized by PSO intelligent algorithm and applied in physical training test. In addition, for the classification of physical training, this paper follows the basic principles of physical training system construction, establishes the physical training measurement index system through the results of expert solicitation, and determines the weights of each index by using the hierarchical analysis method. Through the empirical analysis of the PSO-BP model in this paper, it can be seen that the fitting results of the training samples of male and female students show that the corresponding correlation coefficients of male and female students are 0.99908 and 0.99898, respectively.The errors of the evaluation output values of the physical training measurements and the expected values are within ±3.5, and the prediction error of the BP neural network model optimized by the PSO algorithm is significantly reduced, and the relative errors of the evaluation of male and female students are reduced by 0.988% and 0.833%, respectively. The results show that the results of physical training measurement and evaluation using PSO-BP neural network model are more accurate, which proves that the performance of PSO-BP neural network in this paper has been effectively improved and optimized, and at the same time, it can meet the application requirements of physical training measurement and evaluation.

Journal of Combinatorial Mathematics and Combinatorial ComputingVol. 127a
Openalex Percentile: Top 87%
E-commerce and Technology Innovations
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