Research on Neural Network Cost Prediction Model Based on the Rough Sets Theory in ERP

2008 ◽  
Author(s):  
X.F. Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Rough Sets ◽  
Cost Prediction ◽  
Rough Sets Theory ◽  
Model Based ◽  
Network Cost ◽  
Sets Theory

DECISION MAKING USING HYBRID ROUGH SETS AND NEURAL NETWORKS

2002 ◽  
Vol 12 (06) ◽  
pp. 435-446 ◽  
Author(s):  
YASSER HASSAN ◽  
EIICHIRO TAZAKI ◽  
SHIN EGAWA ◽  
KAZUHO SUYAMA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Rough Sets ◽  
Decision Problem ◽  
Combined System ◽  
Network Systems ◽  
Rough Sets Theory ◽  
Sets Theory

A methodology for using rough sets theory for preference modeling in decision problem is presented in this paper. We will introduce a new method where neural network systems and rough sets theory are completely integrated into a hybrid system and are used cooperatively for decision and classification support. At the first glance, the two methods we discuss have not much in common. But, in spite of the differences between them, it is interesting to try to incorporate both into one combined system, and apply it in the building of a decision support system.

Study on Fault Diagnosis of Power Transformer with Reduction Method of Attribute Significance

2014 ◽  
Vol 1049-1050 ◽  
pp. 665-668
Author(s):  
Hong Li Lv
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rough Sets ◽  
Power Transformer ◽  
Reduction Algorithm ◽  
Rough Sets Theory ◽  
Fault Diagnosis System ◽  
Transformer Fault ◽  
Sets Theory ◽  
Quality Diagnosis

This paper studies the power transformer fault quality diagnosis using rough sets theory and neural network. It is rough sets reduction as the pre-unit of neural network based on reduction algorithm with the attribute significance. The paper describes the reduction algorithm and implementation method detailed. Through the training and testing results with practical data, it is proved that the reduction algorithm with the attribute significance can make the number of input samples shorter, the training speed faster and the diagnostic accuracy higher. The algorithm is feasible and effective for applying to the fault diagnosis system of power transformer.

Equipment Fault Diagnosis Based on Incomplete Rough Sets Theory

2012 ◽  
Vol 490-495 ◽  
pp. 1226-1230
Author(s):  
Yan Qin Su ◽  
Shan Gao ◽  
Ting Xue Xu
Keyword(s):  
Fault Diagnosis ◽  
Test Data ◽  
Rough Sets ◽  
Rough Sets Theory ◽  
Model Based ◽  
Diagnosis Model ◽  
Sets Theory ◽  
Incorrect Data

There are redundant, incomplete and incorrect data in the equipment test data gained by the test equipments. The complete algorithms and attribution reduction algorithms were analyzed and the equipment fault diagnosis model based on Rough Sets Theory was given. Then, some equipment was diagnosed, and the results indicate that the diagnosis is effective and efficient.

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