Coal and rock interface identification based on wavelet packet decomposition and fuzzy neural network

2020 ◽  
Vol 38 (4) ◽  
pp. 3949-3959 ◽  
Author(s):  
Yanbing Liu ◽  
Sanjev Dhakal ◽  
Binyao Hao
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Fuzzy Neural ◽  
Rock Interface

Weighted Algorithm of Multi-Sensor Data Conflict in Coal-Rock Interface Recognition

2011 ◽  
Vol 58-60 ◽  
pp. 1908-1913 ◽  
Author(s):  
Fang Ren ◽  
Zheng Yan Liu ◽  
Zhao Jian Yang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Evidence Theory ◽  
Sensor Data ◽  
Correction Algorithm ◽  
Fuzzy Neural ◽  
Weighted Algorithm ◽  
Rock Interface ◽  
Coal Rock ◽  
Weight Correction

In order to settle such a problem that the multi-sensors data fusion results are not good due to data confliction in the coal-rock interface recognition, the paper first carries out the fusion with D-S evidence theory. The fusion results are not correct when there are high-conflicting in the evidence, so a distance function is introduced and weight fusion correction algorithm is put forward. Through test simulation, fusion results respectively with D-S evidence theory, weight correction algorithm and fuzzy neural network are analyzed. The results show: the good results are achieved in the multi-sensor data conflict of coal-rock recognition through weight fusion correction algorithm, and influence of signal conflict is avoided effectively.

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.

Inverse Control of Cable-driven Parallel Mechanism Using Type-2 Fuzzy Neural Network

2010 ◽  
Vol 36 (3) ◽  
pp. 459-464 ◽  
Author(s):  
Cheng-Dong LI ◽  
Jian-Qiang YI ◽  
Yi YU ◽  
Dong-Bin ZHAO
Keyword(s):  
Neural Network ◽  
Parallel Mechanism ◽  
Fuzzy Neural Network ◽  
Inverse Control ◽  
Fuzzy Neural
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