A Global Discretization and Attribute Reduction Algorithm Based on K-Means Clustering and Rough Sets Theory

2009 ◽  
Author(s):  
Hu Min
Keyword(s):  
Rough Sets ◽  
Attribute Reduction ◽  
Reduction Algorithm ◽  
Rough Sets Theory ◽  
Sets Theory

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.

An Attribute Reduction Algorithms of Expert System Knowledge Acquisition

2011 ◽  
Vol 48-49 ◽  
pp. 187-191 ◽  
Author(s):  
Yong Chang Ren ◽  
Tao Xing ◽  
Ping Zhu
Keyword(s):  
Expert System ◽  
Knowledge Acquisition ◽  
Rough Sets ◽  
Attribute Reduction ◽  
Attribute Importance ◽  
Software Cost ◽  
Rough Sets Theory ◽  
And Performance ◽  
Sets Theory ◽  
Method Construction

Knowledge acquisition is the bottleneck of construction expert system, to provide an accurate inference of knowledge is the key decision-making plan. This article use the rough sets theory, through the rough sets reduction eliminate redundant condition attribute, to achieve the streamlining of the knowledge library. First study the knowledge acquisition, in exposition knowledge hierarchical structure foundation, has given the conceptualization, formal, the knowledge library refinement and so on three knowledge acquisition; and then study attributes reduction algorithms, in the research sets difference and the attribute importance, the reduction algorithms inferential reasoning process's foundation, has given the attribute reduction algorithms six steps. Finally, according to the attributes reduction algorithms and the steps, to estimate the expert system to the function analytic method construction software cost, the composition technology complexity factor of 14 factors reduction. The results showed that the use of rough sets theory to reduce the attributes, can simplify the structure of complex systems, and can effectively maintain the knowledge library structure and performance.

scholarly journals Approximate Accuracy Approaches to Attribute Reduction for Information Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Deshan Liu ◽  
Dapeng Wang ◽  
Deqin Yan ◽  
Yu Sang
Keyword(s):  
Information System ◽  
Information Systems ◽  
Incomplete Information ◽  
Rough Sets ◽  
Attribute Reduction ◽  
New Approach ◽  
Rough Sets Theory ◽  
Incomplete Information Systems ◽  
Sets Theory ◽  
Novel Algorithm

The key problem for attribute reduction to information systems is how to evaluate the importance of an attribute. The algorithms are challenged by the variety of data forms in information system. Based on rough sets theory we present a new approach to attribute reduction for incomplete information systems and fuzzy valued information systems. In order to evaluate the importance of an attribute effectively, a novel algorithm with rigorous theorem is proposed. Experiments show the effect of proposed algorithm.

Compound-Fault Diagnosis of Bearing Based on Order Tracking Wavelet Packet and Rough Sets

2011 ◽  
Vol 130-134 ◽  
pp. 1681-1685 ◽  
Author(s):  
Guang Tian ◽  
Hao Tian ◽  
Guang Sheng Liu ◽  
Jin Hui Zhao ◽  
Li Ping Luo
Keyword(s):  
Fault Diagnosis ◽  
Rough Sets ◽  
Wavelet Packet ◽  
Decision Rules ◽  
Vibration Signals ◽  
Rough Sets Theory ◽  
Equal Angle ◽  
Order Tracking ◽  
Start Up ◽  
Sets Theory

The diagnosis of compound-fault is always a difficult point, and there is not an effective method in equipment diagnosis field, then a new method of compound-fault diagnosis was presented. The vibration signals at start-up in the gearbox are non-stationary signals, and traditional ways of diagnosis have low precision. Order tracking and wavelet packet and rough sets theory are introduced in the compound-fault diagnosis of bearing. First, the vibration signals at start-up were resampled using computer order tracking arithmetic and equal angle distributed vibration signals were obtained, and wavelet packet has been used for equal angle distributed vibration signals decomposition and reconstruction. Then, energy distribution of every frequency band can be calculated according to normalization process. A new feature vector can be obtained, then clear and concise decision rules can be obtained by rough sets theory. Finally, the result of compound-fault example proves that the proposed method has high validity and more amplitude appliance foreground.

Next-day peak electricity price forecasting using NN based on rough sets theory

2008 ◽  
Author(s):  
Hirofumi Toyama ◽  
Tomonobu Senjyu ◽  
Shantanu Chakraborty ◽  
Atsushi Yona ◽  
Toshihisa Funabashi ◽  
...  
Keyword(s):  
Rough Sets ◽  
Electricity Price ◽  
Price Forecasting ◽  
Rough Sets Theory ◽  
Electricity Price Forecasting ◽  
Sets Theory
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