Research on the Safety Evaluation of Large Recreation Facilities Based on BP Neural Network

2014 ◽  
Vol 522-524 ◽  
pp. 881-886
Author(s):  
Yu Zhang ◽  
Zhi Rong Wang ◽  
Qing Qing Zuo ◽  
Xin Dong Zhang ◽  
Xiang Dong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Human Factor ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Qualitative Evaluation ◽  
Evaluation Index System ◽  
Model Based ◽  
Network Training ◽  
Recreation Facilities

A safety evaluation index system regarding to the current safety situation of large recreation facilities in China is established. 13 secondary standard items are built by considerring human factor, equipment factor, environment factor and management factor. The existing safety evaluation of large recreation facilities are conducted by qualitative evaluation methods with highly fuzziness. The evaluation results are uncertain. After the network training, a safety evaluation model based on BP neural network is built. It can reduce the subjectivity of qualitative evaluation effectively with more scientific and objective results. Through the model based on BP neural network, the present safety situation of one large amusement facility is evaluated. The evaluation result is consistent with the actual situation. The method based on BP neural network in the paper provides a new method for safety evaluation of large recreation facilities.

scholarly journals An Evaluation Model for Tailings Storage Facilities Using Improved Neural Networks and Fuzzy Mathematics

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sen Tian ◽  
Jianhong Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Back Propagation ◽  
Safety Evaluation ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Operation Rate ◽  
Network Methods ◽  
Neural Network Algorithm

With the development of mine industry, tailings storage facility (TSF), as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP) and improved back-propagation (BP) neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation.

The Model of Evaluating Teaching Quality Based on BP Neural Network Algorithm

2015 ◽  
Vol 719-720 ◽  
pp. 1297-1301
Author(s):  
Lei Bai ◽  
Xiao Xin Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Hot Spot ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Research Field ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Neural Network Algorithm

Teaching quality evaluation plays a key role for universities to improve its teaching quality and becomes a hot spot research field for related researchers. In this paper, we established the evaluation model of teaching quality based on BP neural network. Firstly an evaluation index system of teaching quality is designed. Then, according to the system we design the structure of BP neural network, determine the parameters and give the algorithm description. Finally, we program and verify the validity of the model in MATLAB environment. The experimental results show that the model can evaluate teaching quality practically by the evaluation index.

scholarly journals Study on Evaluation Model of Emergency Rescue Capability of Chemical Accidents Based on PCA-BP

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jianghong Liu ◽  
Junfeng Wu ◽  
Weisi Liu
Keyword(s):  
Neural Network ◽  
Emergency Management ◽  
Bp Neural Network ◽  
Index System ◽  
Evaluation Model ◽  
Principal Component ◽  
Assessment Model ◽  
Chemical Accidents ◽  
Emergency Rescue ◽  
Model Based

The emergency management of chemical accidents plays an important role in preventing the expansion of chemical accidents. In recent years, the evaluation and research of emergency management of chemical accidents has attracted the attention of many scholars. However, as an important part of emergency management, the professional rescue team of chemicals has few evaluation models for their capabilities. In this study, an emergency rescue capability assessment model based on the PCA-BP neural network is proposed. Firstly, the construction status of 11 emergency rescue teams for chemical accidents in Shanghai is analyzed, and an index system for evaluating the capabilities of emergency rescue teams for chemicals is established. Secondly, the principal component analysis (PCA) is used to perform dimension reduction and indicators’ weight acquisition on the original index system to achieve an effective evaluation of the capabilities of 11 rescue teams. Finally, the indicators after dimensionality reduction are used as the input neurons of the backpropagation (BP) neural network, the characteristic data of eight rescue teams are used as the training set, and the comprehensive scores of three rescue teams are used for verifying the generalization ability of the evaluation model. The result shows that the proposed evaluation model based on the PCA-BP neural network can effectively evaluate the rescue capability of the emergency rescue teams for chemical accidents and provide a new idea for emergency rescue capability assessment.

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