A New BP Neural Network Algorithm and Its Application in English Education Evaluation

Yu Zhike


BP neural network algorithm is a non-linear optimal method and is a hot research field for its powerful simulation calculation ability in various disciplines in recent years, but the algorithm has some shortages such as low convergence which limited the application of the algorithm. The paper improves original BP neural network with immune genetic algorithm to speed up its calculation convergence and presents a new BP neural network algorithm for evaluating English education performance. Firstly, an indicator system for evaluating English education performance is constructed through four aspects of university, teacher, student and teaching effect; Secondly, immune genetic algorithm is used to improve standard BP neural network algorithm, in which the specific measures are taken to integrate BP neural network algorithm and immune genetic algorithm, and the calculation procedures of the improved algorithm is redesigned. Finally data from of three universities are taken for examples to verify the validity and feasibility of the improved algorithm and the experimental results indicate that the algorithm has favorable evaluation results in evaluation accuracy and calculation convergence.


DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.2970


BP neural network algorithm; Immune genetic algorithm; English education evaluation; Evaluation indicator system

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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