Safety Assessment of Transmission Grid Based on Fuzzy Comprehensive Evaluation and BP Neural Network

2010 ◽  
Vol 439-440 ◽  
pp. 528-533
Author(s):  
Yuan Sheng Huang ◽  
Wei Fang ◽  
Cheng Fang Tian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Safety Assessment ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Transmission Grid ◽  
Comprehensive Evaluation Method ◽  
System Method ◽  
Grid Based

In the practice of safety assessment on transmission grid, there is the variation degree of many indexes which can not be accurately described, and fuzzy comprehensive evaluation method can reflect the safety degree of every element. In addition, the combination use of BP neural network and expert system method can determine impact extent of assessment factors on safety of transmission grid and the weight of each factor relative to safety of transmission grid. Therefore, the paper proposes the safety assessment of transmission grid based on BP neural network and fuzzy comprehensive evaluation. Finally, an example is used to prove the method is high precision and practical.

Metro Operation Safety Evaluation

2014 ◽  
Vol 505-506 ◽  
pp. 467-470
Author(s):  
Li Yun Wei ◽  
Yong Bo Lv ◽  
Yuan Ren
Keyword(s):  
Traffic Safety ◽  
Safety Assessment ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Weak Link ◽  
Management Strategies ◽  
Safety Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Comprehensive Evaluation Method ◽  
Urban Rail

In order to evaluate urban rail traffic safety performance comprehensively and to find the weak link to strengthen management, this article, it used the secondary fuzzy comprehensive evaluation method for safety assessment. the qualitative analysis was done to emphasis the structure importance and management strategies are put forward based on the results.

A New Method to Predict Gas Production Based on Fuzzy BP Artificial Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 688-691
Author(s):  
Ran Zhang ◽  
Jun Zhou ◽  
Cheng Yong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Gas Production ◽  
Gas Well ◽  
Well Productivity ◽  
Qualitative Factors ◽  
Productivity Prediction

BP neural network has been successfully used in the gas well productivity prediction, but as a result of neural network is sensitive to the number of input parameters, we had to ignore some factors that is less important to the gas well productivity. In addition, the existing various productivity prediction method cannot consider the influence of some important qualitative factors. This article integrated the advantages of fuzzy comprehensive evaluation and BP neural network, fuzzy comprehensive evaluation method is used to construct the BP neural network's input matrix, and BP neural network learning function is used to solve the connection weights, so as to achieve the aim of predicting gas production. This method not only can consider as many factors influence on gas well production, ut also can consider qualitative factors, so the forecast results of the new model are more realistically close to the actual production situation of reservoirs.

Study on the Safety Assessment of the Mobile Scaffolding System Metal Structure Based on the Onsite Inspection

2012 ◽  
Vol 463-464 ◽  
pp. 1533-1537
Author(s):  
Zhi Bin Zhang
Keyword(s):  
Safety Assessment ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Evaluation Criteria ◽  
Metal Structure ◽  
Fuzzy Comprehensive Evaluation ◽  
Scoring Method ◽  
Assessment Index ◽  
Comprehensive Evaluation Method ◽  
Assessment Index System

This article based on the analysis of the characteristics and the content of the Mobile Scaffolding System (MSS) safety assessment, and according to the on-site inspection of the two MSSs, constructed the MSS metal structure safety assessment index system, and applied the expert scoring method to give the importance degree of every index, finally put forward the fuzzy comprehensive evaluation method for the evaluation criteria.

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