Exercise training load prediction based on improved genetic algorithm
Abstract
With the advancement of digital information age, all kinds of sports also widely used digital mining technology, the technology can promote athletes’ professional level and physical quality, is in the field of sports improve athletes and coaches more effective way, but the bicycle sports for digital mining and research is still in the early state. Cyclists load ability in training is mainly influenced by the body function, the relationship between the two is complicated, this paper is the training load ability and physical function as a research object, explore the specific connection between the two aspects of cyclists, by constructing the cyclist training load prediction model, to apply in the “bicycle team training analysis system”. Strive to promote the overall development of cycling, to provide a basis for the scientific training of talents and training. The cyclist training load prediction model created in this paper can provide a scientific design basis for the training of athletes. When analyzing the body function, this paper mainly analyzes the influence of athletes on conforming sports through 25 aspects such as maximum oxygen intake, functional threshold power and blood oxygen saturation. Because the factors of athletes and the predicted results show non-linear correlation, this paper selects the BP neural network algorithm in the model algorithm, and the adaptive genetic algorithm of the selection operator is adjusted and optimized to clarify the initial weights and thresholds of BP neural network. Finally, the practice has proved that the improved algorithm has a more prominent global optimization ability, and the accuracy of the model has reached 93.28%.
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Copyright (c) 2025 Bin Liu, Junhong Chen, Qian Wang, Wenwen Hu
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