Modeling Analysis of the Relationship between Adolescent Aerobic Exercise and Obesity Reduction Based on Deep Learning

Abstract

In order to explore the modeling analysis of the relationship between adolescent aerobic exercise and obesity reduction, the relationship modeling method of deep learning algorithm is proposed. This study integrates deep learning algorithms and first uses the changes in body shape, weight, BMI index, body fat, body circumference, and other indicators of adolescent obese before and after aerobic exercise as the initial pheromone distribution matrix and introduces random evolution factor and evolutionary drift threshold to establish the objective function of aerobic exercise to reduce adolescent obesity. It also explains the constraint conditions that aerobic exercise has to meet in the reduction of adolescent obesity and introduces particle algorithm to establish a model of optimal aerobic exercise to reduce adolescent obesity. The simulation results show that, under the same number of experiments, the advantages of this method are more obvious. On the overall level, the average modeling error of this method is about 0.053%, while the average of the traditional method is about 0.186%, indicating that the method can reduce the error control within a reasonable range. It proves that deep learning can effectively reflect the modeling analysis of the relationship between adolescent aerobic exercise and obesity reduction.