Abstract
The traditional BP neural network has the disadvantages of easy falling into local minimum and slow convergence speed. Aiming at the shortcomings of BP neural network (BP neural network), an artificial bee colony algorithm (ABC) is proposed to cross-optimize the weight and threshold of BP network parameters. This study is mainly about the application of BP neural network algorithm in English curriculum recommendation technology. It includes the application of BP neural network algorithm in English course recommendation technology, English course teaching design mode, the application of BP neural network algorithm in English course, and the optimal combination of bee colony algorithm and BP neural network. After 4690 iterations, the neural network reaches the target accuracy, and the training is completed. At the same time, the prediction error of the model is less than 10%, which further shows that the performance of the prediction model is good. Therefore, the combination model is recommended in this paper. The results show that the optimization algorithm improves the solution accuracy and speeds up the convergence speed of the network.
Highlights
Aiming at the shortcomings of traditional BP neural network (BP neural network), this paper creatively proposes an artificial bee colony algorithm (ABC) to cross-optimize the weight and threshold of BP network parameters. is study is mainly about the application of BP neural network algorithm in English curriculum recommendation technology. e optimization algorithm improves the solution accuracy and speeds up the convergence speed of the network
A Chemical mechanical polishing (CMP) polishing rate prediction model is established by combining bee colony algorithm and BP neural network. e prediction results of the model are shown in Figures 1 and 9
A CMP polishing rate prediction model is established by combining bee colony algorithm with BP neural network. e prediction results of the model are shown in Figures 1 and 9
Summary
Received 2 September 2021; Revised 20 October 2021; Accepted 13 November 2021; Published 20 December 2021. E traditional BP neural network has the disadvantages of easy falling into local minimum and slow convergence speed. Is study is mainly about the application of BP neural network algorithm in English curriculum recommendation technology. It includes the application of BP neural network algorithm in English course recommendation technology, English course teaching design mode, the application of BP neural network algorithm in English course, and the optimal combination of bee colony algorithm and BP neural network. After 4690 iterations, the neural network reaches the target accuracy, and the training is completed. E results show that the optimization algorithm improves the solution accuracy and speeds up the convergence speed of the network The prediction error of the model is less than 10%, which further shows that the performance of the prediction model is good. erefore, the combination model is recommended in this paper. e results show that the optimization algorithm improves the solution accuracy and speeds up the convergence speed of the network
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