<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, an adjustable neural network model is developed to predict the physical training load of athletes. This paper takes 48 contestants as experimental subjects. Then the average speed prediction method, nonlinear regression model prediction method and neural network model prediction method model are established to predict the training load. This paper uses three different prediction models to compare the training results. The relative error rate calculated by the average rate prediction method is about 23%. The close error rate calculated by the linear regression method is about 32%. The relative error rate calculated by the adjustable neural network is around 8%. The e flexible neural network model has good prediction accuracy.</p>
</abstract>

In this paper, an adjustable neural network model is developed to predict the physical training load of athletes. This paper takes 48 contestants as experimental subjects. Then the average speed prediction method, nonlinear regression model prediction method and neural network model prediction method model are established to predict the training load. This paper uses three different prediction models to compare the training results. The relative error rate calculated by the average rate prediction method is about 23%. The close error rate calculated by the linear regression method is about 32%. The relative error rate calculated by the adjustable neural network is around 8%. The e flexible neural network model has good prediction accuracy.

The Characteristics and Monitoring System of Athletes’ Physical Strength Training Based on Tunable Neuron Mathematical Model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, an adjustable neural network model is developed to predict the physical training load of athletes. This paper takes 48 contestants as...

The Characteristics and Monitoring System of Athletes’ Physical Strength Training Based on Tunable Neuron Mathematical Model

Online veröffentlicht: 23. Mai 2023

Seitenbereich: 3125 - 3134

Eingereicht: 02. Apr. 2022

Akzeptiert: 05. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00025

Schlüsselwörter
Physical training, Tunable neuron mathematical model, Strength training, Optimal load, Prediction method

© 2023 JinAn Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Characteristics and Monitoring System of Athletes’ Physical Strength Training Based on Tunable Neuron Mathematical Model

Online veröffentlicht: 23. Mai 2023

Seitenbereich: 3125 - 3134

Eingereicht: 02. Apr. 2022

Akzeptiert: 05. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00025

SchlüsselwörterPhysical training, Tunable neuron mathematical model, Strength training, Optimal load, Prediction method

© 2023 JinAn Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Physical training, Tunable neuron mathematical model, Strength training, Optimal load, Prediction method