scholarly journals Software Risk Assessment Method based on Fuzzy Neural Network

2015 ◽  
Author(s):  
Hua Wang ◽  
Ben Wang ◽  
Longyong You ◽  
Wentong Zhang
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Fuzzy Neural Network ◽  
Assessment Method ◽  
Risk Assessment Method ◽  
Fuzzy Neural ◽  
Software Risk

A risk assessment method based on RBF artificial neural network - cloud model for urban water hazard

2014 ◽  
Vol 27 (5) ◽  
pp. 2409-2416 ◽  
Author(s):  
Dengfeng Liu ◽  
Dong Wang ◽  
Jichun Wu ◽  
Yuankun Wang ◽  
Lachun Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Cloud Model ◽  
Assessment Method ◽  
Urban Water ◽  
Risk Assessment Method ◽  
Water Hazard ◽  
Artificial Neural

scholarly journals Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network

2022 ◽  
Vol 12 (1) ◽  
pp. 461
Author(s):  
Hui Gao ◽  
Binbin Zang ◽  
Lei Sun ◽  
Liangliang Chen
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fuzzy Neural Network ◽  
Evaluation Method ◽  
High Energy ◽  
Assessment Method ◽  
Fuzzy Evaluation ◽  
Electric Vehicle Charging ◽  
Fuzzy Neural

Electric vehicles have been promoted worldwide because of their high energy efficiency and low pollution. However, frequent charging safety accidents have to a certain extent restricted the development of electric vehicles. Therefore, it is extremely important to accurately evaluate the safety state of EV charging. The paper presents an integrated safety assessment method for electric vehicle charging safety based on fuzzy neural network. The integrated fault model was established by analyzing the correlation between truck–pile–grid. Then the integrated evaluation index was analyzed and sorted out, and the comprehensive fuzzy evaluation method used to evaluate. Following this, the improved GA_BP neural network algorithm was used to calculate the weight. Compared with the evaluation effect before and after the improvement, the simulation results show that the GA_BP neural network has higher accuracy and smaller error than the ordinary BP neural network. Finally, the feasibility and effectiveness of the evaluation method was verified by a case study.

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