Intelligent teaching ability of contemporary college talents based on BP neural network and fuzzy mathematical model

2020 ◽  
Vol 39 (4) ◽  
pp. 4913-4923
Author(s):  
Han He ◽  
Hongcui Yan ◽  
Weiwei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Performance Comparison ◽  
Fuzzy Similarity ◽  
Model Combining ◽  
Comparison Results ◽  
Combining Data ◽  
Teaching Ability

In the evaluation of traditional college talents’ teaching ability, the importance of evaluation indicators lacks evaluation, and the evaluation results are relatively random. In order to improve the evaluation efficiency of university scientific research talents, this study combines BP neural network and fuzzy mathematical theory to build an evaluation model. Combining the talent training process and ability requirements of colleges and universities, a secondary index system is proposed, and the weight of the evaluation index is determined by combining data collection. This paper first normalizes the samples, determines the training and test samples, and then uses trial and error to determine the number of hidden layer neurons. Then use fuzzy mathematics theory to construct fuzzy similarity matrix to describe the fuzzy relationship between factor domain and judgement domain. Calculate membership to get comprehensive evaluation results. Finally, this paper uses statistical methods to draw the results into statistical charts and combines the simulation results to obtain performance comparison results. The feasibility of the model is verified by experimental research, and the model can be applied to practice, and can provide theoretical reference for subsequent related research.

Application of Genetic BP Neural Network in Safety Comprehensive Evaluation of Tailing

2014 ◽  
Vol 687-691 ◽  
pp. 2402-2406
Author(s):  
Song Jiang ◽  
Hui Wen He ◽  
Hong Bo Liu ◽  
Kang Ting Lv
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Threshold Value ◽  
Generalization Ability ◽  
Learning Speed ◽  
Security Levels ◽  
Improved Accuracy

Based on safety assessment factors determined by operation characteristics of a certain tailing ,genetic BP neural network evaluation model is established. To overcome such problems of BP neural network as slow convergence ,poor generalization ability and easy to fall into local minimum value,this paper proposes to use genetic algorithm to optimize threshold value,weights and structure of neural network. Thus,by taking advantage of extensive mapping ability of neural network and global search ability of genetic algorithm,neural network and genetic algorithm will have complementary advantages and the learning speed of network will be accelerated. The application of the described method shows optimized fitting precision,improved accuracy and efficiency ,and enhanced generalization ability of BP neural network. In conclusion,this model can effectively reflect and accurately evaluate non-linear relations between security levels and evaluation factors in tailing.

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