A study on the application of RBF neural network in the estimation of English language and literature teaching quality

Abstract

The quality of education and teaching directly affects the cultivation of talents. Aiming at the problems of low efficiency and low accuracy of current English teaching quality assessment model, a radial basis function (RBF) model combined with genetic algorithm was studied. The model uses genetic algorithm to search RBF parameters, and principal component analysis to reduce the dimension of the index, so as to build the GA-RBF teaching evaluation model. The results show that the mean square error (MSE) of GA-RBF model is 0.2, the precision fluctuation is minimal, and the stability is good. In comparison, GA-RBF mode has a running time of 2.3 s, the evaluation efficiency is faster, and the evaluation accuracy is the highest, reaching 94.28%. The application of this teaching quality evaluation model can improve the quality of school teaching management, enhance the teaching effect, and provide guidance for education reform.