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. 2021 Dec 20:2021:5307646.
doi: 10.1155/2021/5307646. eCollection 2021.

Application of Neural Network Algorithm Combined with Bee Colony Algorithm in English Course Recommendation

Guiting Ren  1
Affiliations

Application of Neural Network Algorithm Combined with Bee Colony Algorithm in English Course Recommendation

Guiting Ren. Comput Intell Neurosci. .

Retraction in

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.

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Conflict of interest statement

The author declares that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Comparison of predicted value and measured value of CMP polishing rate prediction model.
Figure 2
Figure 2
The test effect of each algorithm on f1.
Figure 3
Figure 3
The test effect of each algorithm on f2.
Figure 4
Figure 4
Neuron model.
Figure 5
Figure 5
Feedforward neural network.
Figure 6
Figure 6
Feedback neural network.
Figure 7
Figure 7
Error curve.
Figure 8
Figure 8
The BP neural network reached the preset accuracy after 4690 iterations.
Figure 9
Figure 9
The prediction error of the CMP polishing rate prediction model.
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