Construction of special physical training measurement index system based on artificial intelligence and neural network
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".