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
2013 ◽  
Vol 321-324 ◽  
pp. 2146-2151
Author(s):  
Yong Qiang Zhang ◽  
Xian Min Ma ◽  
Jian Xiang Yang ◽  
Yan Ni Zhang

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.


2014 ◽  
Vol 667 ◽  
pp. 60-63
Author(s):  
Wei Guo ◽  
Zhen Ji Zhang

A performance evaluation system of finance transportation projects is mainly researched, in which the sub-module of the highway projects evaluation, waterway projects evaluation, Passenger stations projects evaluation, Energy saving projects evaluation are incorporated. In addition, the expert knowledge are inserted in the system, the multi-layer neural network and fuzzy-set theory are used to implement Performance Evaluation system of Finance invest Transportation Projects, and the feasibility and effectiveness of the evaluation system are finally verified by practice.


2012 ◽  
Vol 263-266 ◽  
pp. 3378-3381
Author(s):  
Xue Min Zhang ◽  
Zhen Dong Mu

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.


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