Optimization of Skipping Rope Training Method Based on Chaotic Logistics

Abstract

Skipping training belongs to a competitive event, which can not only lose weight but also improve the physical quality of trainers. At present, there are some problems in skipping training, such as unreasonable plan and unsatisfactory actual training effect. The original rough set method cannot solve the analysis of multivariate data in skipping training, and the ability to evaluate the training effect of skipping is poor. Aiming at the problems existing in the training of skipping rope, this paper puts forward a skipping rope training method based on a chaotic logistics algorithm, aiming at improving the training effect of skipping rope. First, the chaos theory is used to classify the training data. Different data classifications correspond to different training results, which eliminates irrelevant information in the training scheme and reduces the amount of single data analysis. Finally, the data after classification are optimized in a single stage, and the optimal results of different training items are obtained and summarized. After the MATLAB test, the chaotic logistics algorithm is superior to the rough set method in accuracy and convergence speed and meets the actual training needs. Therefore, the chaotic logic analysis method proposed in this paper is suitable for the evaluation of skipping training.