Special Factor Adjustment Model Using Fuzzy-Neural Network in Traffic Prediction

2004 ◽  
Vol 1879 (1) ◽  
pp. 17-23 ◽  
Author(s):  
Heng Xiao ◽  
Hongyu Sun ◽  
Bin Ran
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Traffic Prediction ◽  
Adjustment Model ◽  
Special Factor ◽  
Fuzzy Neural

Fuzzy neural network based traffic prediction and congestion control in high-speed networks

2000 ◽  
Vol 15 (2) ◽  
pp. 144-149 ◽  
Author(s):  
Xiang Fei ◽  
Xiaoyan He ◽  
Junzhou Luo ◽  
Jieyi Wu ◽  
Guanqun Gu
Keyword(s):  
Neural Network ◽  
Congestion Control ◽  
High Speed ◽  
Fuzzy Neural Network ◽  
Traffic Prediction ◽  
High Speed Networks ◽  
Fuzzy Neural

Fuzzy-Neural Network Traffic Prediction Framework with Wavelet Decomposition

2003 ◽  
Vol 1836 (1) ◽  
pp. 16-20 ◽  
Author(s):  
Heng Xiao ◽  
Hongyu Sun ◽  
Bin Ran ◽  
Youngtae Oh
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Wavelet Decomposition ◽  
Fuzzy Neural Network ◽  
Wavelet Denoising ◽  
Traffic Prediction ◽  
Denoising Method ◽  
Fuzzy Neural ◽  
Special Factors

The framework of a traffic prediction model that could eliminate noise caused by random travel conditions is investigated. This model also can quantitatively calculate the influence of special factors. The framework combined several artificial intelligence technologies, such as wavelet transform, neural network, and fuzzy logic. The wavelet denoising method is emphasized and analyzed.

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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