Swimming athlete training load prediction method based on PCA-PNN

Abstract

The invention discloses a swimming athlete training load prediction method based on PCA-PNN. The method comprises the steps: step 1, carrying out feature index selection and database construction; step 2, carrying out principal component analysis to construct a PNN network input quantity; and step 3, constructing a PNN swimming athlete training load prediction model. The invention provides the swimming athlete training load prediction method based on PCA-PNN; the method constructs the swimming athlete training load prediction model through the research of the training load impact fusion features of an athlete, so as to evaluate the training arrangement of one swimming athlete.

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 ro...

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