Abstract
Physical education is considered as the essential part of health plan and also reflects the higher education system. A teacher with physical education is accountable to teach physical education and/or health education to the students. This study has proposed a new approach for teaching evaluation with college physical education in universities. By combining neural network with genetic algorithm (GA), the approach that we proposed can improve the general traditional BP network, such as global convergence and training time. The model uses MATLAB software for empirical research work. According to MATLAB simulation and experiments, it was revealed that the combining neural network with GA algorithm has effective application prospects for the teaching evaluation with college physical education in universities.
Highlights
At present, we always do the teaching evaluation through different methods and approaches, as the teaching quality has always been the focus of social attention
We introduce the Genetic algorithm (GA) into the BP neural network for the evaluation model, which is a further optimization application of BP neural network
Various research studies have been conducted associated to physical education
Summary
We always do the teaching evaluation through different methods and approaches, as the teaching quality has always been the focus of social attention. (d) It is difficult to make an accurate evaluation by using traditional methods to evaluate the results of some indicators, and the calculation is complex, the solution is cumbersome, and these algorithms lack of self-learning ability [14,15,16] To overcome these problems, this study has proposed a novel approach for teaching evaluation with college physical education in universities. Scientific Programming network with GA algorithm has operational applications for the teaching evaluation with college physical education in universities. Where ωi is the weight from the input layer node i to the middle layer node j and pi represents the ith factor of the sample, i.e., the ith teaching quality evaluation index. Where q is the actual output value of the sample, that is, the calculated teaching quality evaluation value and ωj is the connection weight from the middle layer node i to the output layer node
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