An optimized BP neural network model for teaching management evaluation

Author(s):  
Xinhua Yang ◽  
JiaJia Zhou ◽  
DaoQun Wen

To improve the effectiveness and intelligence of university teaching management evaluation, the particle swarm optimization BP neural network algorithm is applied to the analysis of university teaching management evaluation data. BP neural network is used to model the evaluation index of teaching management, and then particle swarm optimization is used to optimize the weight and threshold of the neural network transfer function to ensure that the output of the BP neural network can obtain the global optimal solution. The experimental results show that the proposed algorithm has a good fit between the predicted value and the actual value of the evaluation object of teaching management in Colleges and universities, and has a strong promotion value.

2013 ◽  
Vol 655-657 ◽  
pp. 969-973
Author(s):  
Bo Li ◽  
Song Xin Shi ◽  
Shi Wang

A novel image recognition method based on chaotic-particle swarm-optimization-neural network algorithm was presented. The chaotic mapping mechanism and particle swarm algorithm were used to optimize the weight and threshold of BP neural network which was applied to the recognition of image. The simulation results show this new method can overcome the problems that BP neural network is easy to fall into local optimum and sensitive to the initial value, and has better recognition rate and stronger robustness.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 184656-184663
Author(s):  
Xiaoqiang Tian ◽  
Lingfu Kong ◽  
Deming Kong ◽  
Li Yuan ◽  
Dehan Kong

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