Affiliations: [a]
Wuhan University of Science and Technology, Wuhan, Hubei | [b] Hubei University of Police, Wuhan, Hubei | [c] Henan Polytechnic University, Jiaozuo, Henan
Correspondence:
[*]
Corresponding author. He Being, E-mail: [email protected].
Abstract: Aiming at the problem of low accuracy of the current English interpretation teaching quality evaluation, a teaching quality evaluation method based on a genetic algorithm (GA) optimized RBF neural network is proposed. First, the principal component analysis is used to select the teaching quality evaluation index, and then design The RBF neural network teaching evaluation model is used, and GA is used to optimize the initial weights of the RBF neural network. Experimental results show that this method can effectively evaluate the quality of English interpretation teaching, and has high accuracy and real-time performance.
Keywords: English interpretation teaching, quality evaluation, RBF neural network, genetic algorithm, principal component analysis
