Highway Traffic Accident Detection Method with Fuzzy Neural Network

2007 ◽  
Author(s):  
Feng-yuan Wang ◽  
Bing-xi Huang ◽  
Da-yi Qu ◽  
Zhao-wen Zhang ◽  
Li-xin Liu
Keyword(s):  
Neural Network ◽  
Traffic Accident ◽  
Fuzzy Neural Network ◽  
Detection Method ◽  
Highway Traffic ◽  
Fuzzy Neural ◽  
Accident Detection ◽  
Traffic Accident Detection

Traffic Accident Prediction on Provincial Road Using Fuzzy Neural Network

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Fei Zhao ◽  
Liping Fu ◽  
Ming Zhong ◽  
Ge Cui ◽  
Chengpeng Zheng
Keyword(s):  
Neural Network ◽  
Traffic Accident ◽  
Fuzzy Neural Network ◽  
Accident Prediction ◽  
Fuzzy Neural

Research of Test Model for Eggshell Crack Detection

2013 ◽  
Vol 846-847 ◽  
pp. 655-658
Author(s):  
Jian Yang ◽  
Chun Yan Xia ◽  
He Pan ◽  
Ying Shi ◽  
Xiu Ying Li
Keyword(s):  
Neural Network ◽  
Crack Detection ◽  
Fuzzy Neural Network ◽  
Detection Method ◽  
Experimental Result ◽  
Test Model ◽  
Inference Rules ◽  
Characteristic Parameters ◽  
Fuzzy Neural ◽  
Set Up

In order to realize the precise identification of eggshell crack, we design eggshell cracks detection method based on image processing and fuzzy neural network. Firstly this method gets two pieces image of eggs and processes, and then counts number of the same gray pixel. Determine five characteristic parameters as the input of fuzzy neural network. Set up a fuzzy neural network. Its structure is 5-10-1. Eggshell cracks and noise in egg images were distinguished using automatically learning and inference rules of fuzzy neural network. Use 147 groups of parameters for training network and rest 58 sample for verifying. Experimental result shows that the model can meet actual testing requirements with fast, stable, high precision and good robustness, easy to implement. Its precision reached 94.55%.

scholarly journals Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jin Zhang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Detection Method ◽  
Automatic Detection ◽  
Convergence Time ◽  
Series Data ◽  
Table Tennis ◽  
Trajectory Prediction ◽  
Acceptable Error ◽  
Fuzzy Neural

In the system design of table tennis robot, the important influencing factors of automatic detection of technical and tactical indicators for table tennis are table tennis rotation state, trajectory, and rebound force. But the general prediction algorithm cannot process the time series data and give the corresponding rotation state. Therefore, this paper studies the automatic detection method of technical and tactical indicators for table tennis based on the trajectory prediction using the compensation fuzzy neural network. In this paper, the compensation fuzzy neural network algorithm which combines the compensation fuzzy algorithm and recurrent neural network is selected to construct the automatic detection of technical and tactical indicators for table tennis. The experimental results show that the convergence time of the compensation fuzzy neural network is shorter, the training time is shortened, and the prediction accuracy is improved. At the same time, in terms of performance testing, the model can accurately distinguish the influence of table tennis rotation state and rebound on table tennis motion estimation, so as to improve the accuracy of motion trajectory prediction. In addition, the accuracy of trajectory prediction will be improved with the increase of input data. When the number of data reaches 30, the trajectory prediction error is within the actual acceptable error range.

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.

Sign in / Sign up

Export Citation Format

Share Document