Open AccessTRAINING METHOD OF SPORTS ATHLETES USING THE NONLINEAR SYSTEM OF MOVING HUMAN BODY COMPETITIVE ABILITY
Author(s) -
Chaohu He,
Liaokun Ye,
Hani Jamal Sulaimani,
Wei Hu
Publication year - 2022
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x2240093x
Subject(s) - nonlinear system , athletes , process (computing) , computer science , training (meteorology) , mode (computer interface) , artificial intelligence , simulation , physical therapy , human–computer interaction , medicine , physics , quantum mechanics , meteorology , operating system
The purpose is to optimize the training methods of athletes and improve their competitive ability. First, the corresponding research model is established by analyzing the influencing factors of the athlete training system and combining with the dynamic nonlinear theory. Next, based on the deterministic learning theory, the dynamic nonlinear state identification model of athletes is established to obtain the state information of athletes in the process of competitive sports training and competition. Then, based on the established nonlinear state model, the nonlinear staged athlete training mode is designed. The experimental results show that the designed gait recognition algorithm based on deterministic learning can effectively collect the nonlinear signals generated by the movement process; the accuracy of gait recognition is about 90%, and it is less affected by the training and test datasets. The nonlinear staged training mode can effectively improve the overall index of maximum strength and strength endurance of tennis players, and effectively improve the training effect of players. This exploration provides a reference for the study of nonlinear competitive ability training mode of sports athletes.