Battlefield Wireless Communication Interference Threat Assessment Method Based on Hierarchical Fuzzy Neural Network

2018 ◽  
Author(s):  
Jiankun Ma ◽  
Tao Liang ◽  
Haidong Cai
Keyword(s):  
Neural Network ◽  
Wireless Communication ◽  
Fuzzy Neural Network ◽  
Threat Assessment ◽  
Assessment Method ◽  
Fuzzy 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.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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