Rough Set Fuzzy Neural Network Fault Diagnosis for the over Current Detection of Coal Mining Scraper

2013 ◽  
Vol 321-324 ◽  
pp. 2146-2151
Author(s):  
Yong Qiang Zhang ◽  
Xian Min Ma ◽  
Jian Xiang Yang ◽  
Yan Ni Zhang
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Neural Network ◽  
Rough Set Theory ◽  
Driving System ◽  
The Neural Network ◽  
Scraper Conveyor ◽  
Fuzzy Neural ◽  
Network Fault

In this paper the rough set fuzzy neural network is used to monitor the over current fault problem of the mining scraper conveyor motor driving system. The phase current signals are input into the neural network, and then the current signals are processed with fuzzy logic set theory for optimization. Because too many rules may lead to complex computation, the rough set theory is used to reduce the rules after the signal characteristics are extracted. The simulation results show that the precision and reliability of motor driving system of the mining scraper conveyor can be improved by this method.

Integrating rough set theory and fuzzy neural network to discover fuzzy rules

2003 ◽  
Vol 7 (1) ◽  
pp. 59-73 ◽  
Author(s):  
Shi-tong Wang ◽  
Dong-jun Yu ◽  
Jing-yu Yang
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Neural Network ◽  
Rough Set Theory ◽  
Fuzzy Rules ◽  
Fuzzy Neural

Design of Neural Network Based on Rough Set

2012 ◽  
Vol 263-266 ◽  
pp. 3378-3381
Author(s):  
Xue Min Zhang ◽  
Zhen Dong Mu
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Sample Test ◽  
Low Efficiency ◽  
The Right ◽  
Test Module

After years of development, the neural network classification, clustering and forecasting applications have a lot of development, but the neural network has the inevitable defects, if you enter the attribute set, the classification boundaries are not clear, convergence low efficiency and accuracy, there may even be the state does not converge, using rough set theory, the right value to modify the function to be modified, and joined the contradictions sample test module, after the use of EEG to verify reached the deletion of number of features and the purpose to improve the classification accuracy.

Financial Crisis Prediction Based on Intelligent Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 5000-5003 ◽  
Author(s):  
Xin Yun Liu
Keyword(s):  
Neural Network ◽  
Financial Crisis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Intelligent Algorithm ◽  
Actual System ◽  
Training Time ◽  
The Neural Network ◽  
Neural Network Algorithm

The typical intelligent algorithm based financial crisis prediction is the neural network. But training time of the neural network is too long to apply to the actual system. The paper applies the rough set theory into the artificial neural network based financial crisis prediction. The paper proposes an improved neural network algorithm. The rough set theory is used to reduce the financial indexes and samples. The numbers of inputs and training samples are decreased and the training time of neural network is shortened. The empirical analysis shows that the method can obviously accelerate the training speed of the neural network.

Diesel Engine Fault Diagnosis Based on Rough Sets

2013 ◽  
Vol 722 ◽  
pp. 276-281
Author(s):  
Hong Xia Pan ◽  
Jing Yi Tian
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Set Theory ◽  
Rough Set ◽  
Cylinder Head ◽  
Rough Set Theory ◽  
Vibration Signal ◽  
Fault Classification ◽  
The Neural Network

This paper introduces the rough set theory and ROSETTA software characteristics, gives a diesel engine fault diagnosis system based on rough set theory and the vibration signal of cylinder head. Taking a certain type large power diesel engine as an example, the first to be extracted from the cylinder head vibration signal wavelet packet de-noising and time-frequency domain analysis, constructed eigenvalue for fault diagnosis, then use ROSETTA software reduction feature attributes, finally completed fault pattern classification through the neural network. By comparing the output results of the neural network before and after processing by the ROSETTA software, show that rough set theory can optimize the feature attributes, effectively reduce the input of the neural network nodes, and improve the fault classification accuracy.

The Application of Tolerant Rough Set Neural Network to Fighter Fault Diagnosis

2013 ◽  
Vol 341-342 ◽  
pp. 809-814
Author(s):  
Guo Qiang Sun ◽  
Hong Li Wang ◽  
Jun Tao ◽  
Xu Bing Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Useful Knowledge ◽  
The Neural Network ◽  
Catch Up ◽  
Simulation Results ◽  
Adaptive Ability

Conventional rough set theory is based on indiscernibility relation, which lacks the adaptive ability to data noise or data missing. Furthermore, it may present qualitatively whether or not the faults exist, but it cant compute accurately the value of the faults. Though the neural network has ability of approximating unknown nonlinear systems, but it cant distinguish the redundant knowledge from useful knowledge, so its classification ability cant catch up with the rough set classifier. This paper combines the rough set theory and the tolerant rough set neural network to diagnose the rudder faults of fighter, which solves well the problem of fault diagnosis and fault degree computation. Simulation results demonstrate the effectiveness of the proposed method.

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