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Optimization of training load and recovery strategies for track and field sprinters by genetic algorithm

Qinhai Wang

Qinhai Wang

College of Physical Education, Huaqiao UniversityQuanzhou, China
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Wang, Qinhai
  
19 mar 2025
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Publicado en línea: 19 mar 2025
Recibido: 09 nov 2024
Aceptado: 11 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0543
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© 2025 Qinhai Wang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Structure of BP neural network
Structure of BP neural network

Figure 2.

Genetic algorithm solution process
Genetic algorithm solution process

Figure 3.

Data categories of cycling teams
Data categories of cycling teams

Figure 4.

Network structure topology of training load forecasting model
Network structure topology of training load forecasting model

Figure 5.

Algorithm flow
Algorithm flow

Figure 6.

Neural network predictive value and true value
Neural network predictive value and true value

Figure 7.

Neural network performance tracking curve
Neural network performance tracking curve

Figure 8.

Percentage of prediction error
Percentage of prediction error

Figure 9.

A change in the load and physical condition of an athlete
A change in the load and physical condition of an athlete

Figure 10.

Typical load input time model response
Typical load input time model response

Figure 11.

The predicted value and test value of a tested physical condition
The predicted value and test value of a tested physical condition

The average square error comparison table of the neural network model

Run Frequency Bp neural network Ga-bp neural network This model
1 0.00371 0.00304 0.00183
2 0.00491 0.00279 0.00143
3 0.0044 0.00326 0.0017
4 0.00456 0.00349 0.0017
5 0.00433 0.00334 0.00141
6 0.00372 0.00305 0.00151
7 0.00348 0.00252 0.0019
8 0.00327 0.0031 0.00156
9 0.00349 0.00251 0.00192
10 0.00402 0.00283 0.00115
11 0.00317 0.0033 0.00195
12 0.0047 0.00301 0.00101
13 0.00374 0.00282 0.00195
14 0.00325 0.00212 0.00155
15 0.00469 0.00263 0.00199
16 0.00397 0.00337 0.00169
17 0.00402 0.00282 0.00155
18 0.00331 0.00299 0.00158
19 0.00429 0.00268 0.00184
20 0.00321 0.00218 0.00171
21 0.00374 0.00201 0.00141
22 0.00416 0.00313 0.00187
23 0.0043 0.00261 0.00138
24 0.0043 0.00234 0.00161
25 0.00456 0.0034 0.00131
26 0.00429 0.00234 0.00127
27 0.00359 0.00231 0.00118
28 0.00362 0.00326 0.00122
29 0.00374 0.00294 0.00156
30 0.00485 0.00309 0.00161
31 0.00404 0.00305 0.00179
32 0.00383 0.00302 0.00146
33 0.00442 0.00294 0.00199
34 0.00374 0.00208 0.00184
35 0.00304 0.00337 0.00109
36 0.00438 0.00234 0.00163
37 0.00324 0.00282 0.00195
38 0.00422 0.00329 0.00161
39 0.0036 0.00318 0.00189
40 0.00414 0.00226 0.00188

Model parameter estimation

N Duration of motion Ka Kf Ta Tf G R2 Prediction similarity
1 4 0.098 0.056 0.009 0.174 9.823 0.837 0.927
2 4 0.165 0.037 0.016 0.184 11.055 0.856 0.864
3 4.5 0.161 0.034 0.005 0.169 12.637 10.562 0.935
4 5 0.159 0.049 0.014 0.152 11.762 0.954 0.987
5 5 0.116 0.048 0.018 0.113 12.739 0.852 0.773
6 5 0.174 0.043 0.016 0.152 12.685 0.843 0.992
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Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros
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