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.

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.

Study of Signal-Noise Separation Based on the Fuzzy Neural Network

2014 ◽  
Vol 687-691 ◽  
pp. 3822-3827
Author(s):  
Bin Liang ◽  
Yan Ping Bai
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Signal To Noise Ratio ◽  
Reference Signal ◽  
Signal Noise ◽  
Evaluation Indexes ◽  
Low Snr ◽  
Fuzzy Neural ◽  
S Model ◽  
Snr Gain

This paper introduces the basic mathematical model of fuzzy neural network and T-S model. It uses the fuzzy neural network for targeted emulational signal-noise separation and presents evaluation indexes of the fuzzy neural network’s denoising effect, analyzes its mean square error (MSE), signal-to-noise ratio (SNR), SNR gain, and similarity of signal and theoretical reference signal after denoising. The simulation results show that this algorithm has prominent effect of separation under high and low SNR environment. At last, the experiments for the second lake of Fenhe also validated the superiority and effectiveness of this algorithm.

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.

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