Research on the Application of Fuzzy Neural Network in the Automobile Reliability

2010 ◽  
Vol 136 ◽  
pp. 77-81
Author(s):  
Yan Zhong Men
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Evaluation Method ◽  
Theoretical Foundation ◽  
Field Conditions ◽  
Maintenance Strategy ◽  
Automobile Engines ◽  
Fuzzy Neural ◽  
Engine Failure

The data of engine failures about automobile operating in field conditions was collected by running engine failure tracking tests. An evaluative model was established using the theory of Fuzzy Neural Network (FNN) for the automobile “using reliability, based on the data of failures about automobiles, finally, the evaluative result for the automobile” using reliability was obtained by evaluative model of reliability. The result of research can be used as references for the improvement of reliability and maintainability of automobile engines, and for the establishment of maintenance strategy, and it also laid a theoretical foundation for studying the improvement of evaluation method for the automobiles' reliability as well.

Research on the Application of Fuzzy Neural Network in the Tobacco Heights Department of Tractor Reliability

2012 ◽  
Vol 152-154 ◽  
pp. 1899-1903
Author(s):  
Yan Zhong Men ◽  
De Tang Zou
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Evaluation Method ◽  
Theoretical Foundation ◽  
Field Conditions ◽  
Maintenance Strategy ◽  
Fuzzy Neural ◽  
Engine Failure

The data of engine failures about tobacco Heights Department of tractor operating in field conditions was collected by running engine failure tracking tests. An evaluative model was established using the theory of Fuzzy Neural Network(FNN) for the tractor ' using reliability, based on the data of failures about tractors, the observed values of the reliability standards were made out and the degree of subordination was figured out, finally, the evaluative result for the tractors ' using reliability was obtained by evaluative model of reliability. The result of research can be used as references for the improvement of reliability and maintainability of tractor engines, and for the establishment of maintenance strategy, and it also lay a theoretical foundation for studying the improvement of evaluation method for the tobacco Heights Department of tractor ' reliability as well.

scholarly journals Water Quality Evaluation Method Based on a T-S Fuzzy Neural Network—Application in Water Environment Trend Analysis of Taihu Lake Basin

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3127
Author(s):  
Wei Ye ◽  
Wei Song ◽  
Chen-Feng Cui ◽  
Jia-Hao Wen
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Fuzzy Neural Network ◽  
Evaluation Method ◽  
Taihu Lake ◽  
Water Environment ◽  
Lake Basin ◽  
Neural Network Models ◽  
Taihu Lake Basin ◽  
Fuzzy Neural

In response to the problems of large computational volume and tedious computational process of fuzzy integrated evaluation, and general neural network models without clear water quality training criteria, this paper organically combines fuzzy rules, affiliation function, and neural network, and proposes a comprehensive method for the evaluation of water quality based on a T-S fuzzy neural network. On the three water quality monitoring data of six national key monitoring stations in Taihu Lake Basin, three evaluation methods—the one-factor evaluation method, the fuzzy integrated evaluation method, and the T-S fuzzy neural network evaluation method—were used to comprehensively evaluate water environment quality, and the results showed that the T-S fuzzy neural network method has the advantages of convenient calculation, strong applicability, and scientific results.

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.

Evaluating the Environmental Cost Internalization Based on the Fuzzy Neural Network

2014 ◽  
Vol 1073-1076 ◽  
pp. 495-499
Author(s):  
Xiang Song Meng ◽  
Yi Yao Zhu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Environmental Cost ◽  
Fuzzy Evaluation ◽  
Analysis Model ◽  
Cost Index ◽  
Fuzzy Neural ◽  
Environment Cost

Internalization of environment cost assessment measures the level of an enterprise’s environmental cost internalization. It’s also the basis of carrying out recycling economic in an enterprise. First of all, we established an environmental cost analysis model, in line with which we build the internalization of environment cost index system. Then adopting comprehensive evaluation method basing on fuzzy neural network can help us assess the effect brought by the internalization of environment cost. Finally, we conducted an experiment which comparing fuzzy neural network with the fuzzy evaluation of environment cost objectively. So we can think it’s an effective method.

scholarly journals Speed Grade Evaluation of Public-Transportation Lines Based on an Improved T-S Fuzzy Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shunfeng Zhang ◽  
Peiqing Li ◽  
Biqiang Zhong ◽  
Jin Wu
Keyword(s):  
Neural Network ◽  
Public Transportation ◽  
Large Scale ◽  
Fuzzy Neural Network ◽  
Evaluation Method ◽  
Rail Transit ◽  
Transit Planning ◽  
Fuzzy Neural ◽  
S Model ◽  
Speed Grade

This paper proposes an evaluation method based on a T-S fuzzy neural network for evaluating the speed grade of public-transport lines in the context of large-scale rail-transit planning and construction in Hangzhou. The six-dimensional data of morning peak/evening peak average speed, average speed at peak, average station distance, proportion of dedicated lanes, and nonlinear coefficients were selected as input data for the neural network to output the operating speed grade of bus lines. Improving and optimizing the membership function of the Takagi–Sugeno (T-S) model improves its predicted result accuracy compared to a traditional T-S model. The line data of 28 typical trunk lines or expressways in Hangzhou were used as an example; the results demonstrate that the speed grade evaluation method based on an improved T-S fuzzy neural network can effectively and quickly evaluate the speed grade of Hangzhou public-transportation lines. This paper presents a novel analysis and method for large-scale rail-transit planning and evaluation of urban public-transport lines. The aim is to provide practical instruction for the subsequent optimization of public-transportation lines in Hangzhou.

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