Title: Research on the application of improved BPNN algorithm in music education quality evaluation algorithm

Authors: Bin Sun

Addresses: School of Sociology and Anthropology, Xiamen University, Xiamen, 361000, China

Abstract: The objective, fair, accurate and reasonable evaluation of teaching quality is the premise to improve the teaching quality of colleges and universities. In this study, based on the traditional BP neural network and the adaptive mutation genetic algorithm, a BP neural network music teaching quality evaluation model with improved adaptive genetic algorithm is proposed. The performance of this model is compared with that of the traditional BP neural network model. The experimental results show that the mean square error sum of the final convergence of the proposed model is 0.15, the convergence speed is increased by 89%, and the mean square error sum is reduced by 75%; After the 21st iteration, the convergence is completed and reaches a stable state. Combined with the above model comparison data and prediction results, it shows that the model can well complete the teaching evaluation prediction.

Keywords: BP; Music teaching; evaluation model; teaching quality; genetic algorithm; self-adaption; college education.

DOI: 10.1504/IJKBD.2023.133325

International Journal of Knowledge-Based Development, 2023 Vol.13 No.2/3/4, pp.214 - 230

Received: 24 Aug 2022
Accepted: 29 Sep 2022
Published online: 12 Sep 2023 *

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