A Prediction Method of Swimmer's Training Load Based on pca-pnn

Abstract

A PCA-PNN-based swimmer training load prediction method, Step1: feature index selection and database construction; Step2: principal component analysis to build PNN network input; Step3: build PNN swimmer training load prediction model. The present invention provides a PCA-PNN-based swimmer training load prediction method. The method constructs a swimmer's training load prediction model to evaluate a swimmer's training arrangement by studying how the athlete's training load affects fusion features.

Description

technical field [0001] The invention belongs to the field of athlete training load prediction methods, in particular to a PCA-PNN-based swimmer training load prediction method. Background technique [0002] In competitive swimming, the development of athletes' competitive ability depends not only on whether the selection method of athletes is scientific, but also on whether the training methods and methods during years of training are reasonable, and whether the exercise load is properly arranged. Training is an important daily content in an athlete's career. Athletes can improve their competitive level through reasonable training, but too much training will cause injuries to athletes, and too little training will not achieve the training effect. At the same time, if the same training method and load arrangement are used for athletes in different states, then the training will not have the desired effect, and even if it is excessive, it will cause damage to the technique and...

AI Technical Summary

Problems solved by technology

[0003] However, the existing training load arrangement for swimmers does not have a complete and reliable framework, and does not give full play to the important role of excellent coaches in popularizing scientific swimming sports training. Therefore, the applicant designs a swimmer training based on PCA-PNN load forecasting method

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