Analysis of Feasibility Modeling of Multimode Physical Fitness Training for Long-Distance Runners

Xu Xia

doi:10.1155/2021/5817396

Abstract

Because the traditional multimode feasibility modeling analysis method of physical fitness training for long-distance runners has the problems of long modeling time and low modeling accuracy, a new multimode feasibility modeling analysis method for physical fitness training of long-distance runners is proposed. The improved LLE (local linear embedding) method is used to reduce the dimensionality of the training data for the physical fitness of long-distance runners. According to the processing results, the information theory is used to analyze the information content of the physical fitness training features of the long-distance runners, the information entropy of each feature is calculated, and the long-distance runners are extracted. Athlete’s physical fitness enhancement training characteristics, combined with quantitative regression analysis method to carry out the information regression analysis of the long-distance runners’ physical fitness training multimode statistical sequence, construct the feasibility evaluation model of the long-distance runners’ physical fitness training multimode and complete the feasibility of the long-distance runners’ physical fitness training multimode feasibility study Mode analysis. The simulation experiment results show that the proposed method has higher accuracy and shorter modeling time for multimode feasibility modeling of physical fitness training for long-distance runners.

Highlights

In long-distance running, aerobic oxidation is the main body energy metabolism of athletes, supplemented by anaerobic metabolism
In order to verify the performance of the multimode feasibility modeling and analysis method of long-distance runners’ physical fitness enhancement training proposed in this paper in practical application, the long-distance runners of a sports school are selected as the experimental objects, and the joint programming design of Visual C + + and MATLAB 7 is adopted
The results show that the feature extraction accuracy of the multimode feasibility modeling analysis method of long-distance runners’ physical enhancement training proposed in this paper is higher than that of the methods in literature [5] and literature [6], because before the feature extraction of long-distance runners’ physical enhancement training, the feature extraction accuracy of long-distance runners’ physical enhancement training proposed in this paper is higher than that of literature [5] and literature [6]

Summary

Introduction

In long-distance running, aerobic oxidation is the main body energy metabolism of athletes, supplemented by anaerobic metabolism. In the above formula, j is the characteristic sampling point for multimode feasibility evaluation of physical fitness enhancement training for long-distance runners under a certain embedding dimension i, denoted as follows: yij = axij − bi: ð15Þ. In the above formula, a = D, combined with the fuzzy feature analysis method, obtains the joint distribution probability of the multimode feasibility statistical sample sequence xðt + τÞ in the state estimation region of the long-distance runners, which is random sampling. The information characteristics of multimode feasibility evaluation of physical fitness enhancement training for long-distance runners are as follows: IðτÞ = −〠pijðτÞ ij ln pijðτÞ : pipj ð17Þ. When IðτÞ = 0, xðt + τÞ is the statistical sample sequence of multimode feasibility evaluation of longdistance runners’ physical fitness enhancement training If it meets the convergence solution, it means that it is unpredictable, that is, xðtÞ, xðt + τÞ is independent and completely uncorrelated. Assuming that the probability random variable of the multimode feasibility time series xðtÞ appearing in the distribution interval i satisfies the convergence condition, the multimode feasibility model of long-distance runners’ physical enhancement training is completed

Simulation Experiment Analysis

Findings

Conclusion