Rough Set on Two Universal Sets and Knowledge Representation

2014 ◽  
pp. 79-108 ◽  
Author(s):  
Debi Acharjya
Keyword(s):  
Knowledge Representation ◽  
Rough Set

Degree Dominance Interval Relation-Based RSM in Intuitionistic Fuzzy Decision Systems

2011 ◽  
Vol 48-49 ◽  
pp. 357-361
Author(s):  
Bing Huang
Keyword(s):  
Knowledge Representation ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Dominance Relation ◽  
Fuzzy Decision ◽  
Decision Systems ◽  
Intuitionistic Fuzzy ◽  
Interval Relation

By introducing a degree dominance relation to dominance interval intuitionistic fuzzy decision systems, we establish a degree dominance interval rough set model (RSM), which is mainly based on replacing the indiscernibility relation in classical rough set theory with the degree dominance interval relation. To simplify knowledge representation and extract some nontrivial simpler degree dominance interval intuitionistic fuzzy decision rules, we propose two attribute reductions of the degree dominance interval intuitionistic fuzzy decision systems that eliminate the redundant condition attributes that are not essential from the viewpoint of degree dominance interval intuitionistic fuzzy decision rules.

scholarly journals Ranking for Objects and Attribute Reductions in Intuitionistic Fuzzy Ordered Information Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
Xiaoyan Zhang ◽  
Weihua Xu
Keyword(s):  
Information Systems ◽  
Knowledge Representation ◽  
Rough Set ◽  
Attribute Reduction ◽  
Fuzzy Relation ◽  
Discernibility Matrix ◽  
Intuitionistic Fuzzy ◽  
Approximation Operators

We aim to investigate intuitionistic fuzzy ordered information systems. The concept of intuitionistic fuzzy ordered information systems is proposed firstly by introducing an intuitionistic fuzzy relation to ordered information systems. And a ranking approach for all objects is constructed in this system. In order to simplify knowledge representation, it is necessary to reduce some dispensable attributes in the system. Theories of rough set are investigated in intuitionistic fuzzy ordered information systems by defining two approximation operators. Moreover, judgement theorems and methods of attribute reduction are discussed based on discernibility matrix in the systems, and an illustrative example is employed to show its validity. These results will be helpful for decisionmaking analysis in intuitionistic fuzzy ordered information systems.

Rough Set Data Analysis System and Its Applications in Machinery Fault Diagnosis

2004 ◽  
Vol 471-472 ◽  
pp. 850-854
Author(s):  
L.N. Hao ◽  
Wen Lin Chen ◽  
X.F. Zhang ◽  
Wan Shan Wang
Keyword(s):  
Data Analysis ◽  
Fault Diagnosis ◽  
Knowledge Representation ◽  
Rough Set ◽  
Rough Set Theory ◽  
Base Modification ◽  
Rule Acquisition ◽  
Analysis System ◽  
Data Analysis System ◽  
Machinery Fault Diagnosis

The characteristics of fault diagnosis are as follows. First, features extraction is the key of improving diagnosis efficiency and correct rate. Secondly, fault diagnosis method based on rule reasoning has a wide application, but rule acquisition is one of the bottlenecks. Thirdly, rule modification is a key question of solving the real-time rule acquisition in the dynamic environments, and a primary question of knowledge base modification of expert system, etc. In this paper, Rough Set Theory (RST) was used to solve the key problems of machinery fault diagnosis, and a Rough Set Data Analysis System (RSDAS) was developed. RSDAS was used to implement rule generation automation & rule modification based on RST such as indiscernibility relation and knowledge reduction method, depicted importance of different attributes in knowledge representation, and reduced knowledge representation space. The method of fault diagnosis using RSDAS was summarized. The experiment results approved the feasibility and the high precision of RSDAS. Therefore, we can use RADAS to machinery fault diagnosis.

Decision Process Knowledge Acquisition through Integration of Rough Set and Ontology Technology

2010 ◽  
Vol 26-28 ◽  
pp. 176-180
Author(s):  
Xin Hua Liu ◽  
Xu Mei Liu ◽  
You Fu Hou
Keyword(s):  
Knowledge Representation ◽  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Decision Process ◽  
Rough Set Theory ◽  
Process Knowledge ◽  
Intelligent Approach ◽  
Computing Approach ◽  
Knowledge Ontology

In order to realize the sharing and reuse of process knowledge, an intelligent approach for process knowledge acquisition based on rough set theory and ontology technology was proposed. The framework of process knowledge acquisition was put forward and process knowledge acquisition system was defined. An information complementation method was presented and the algorithm for automating process knowledge acquisition was designed. Moreover, ontology technology was applied in process knowledge representation to resolve the problem of process knowledge reliability and consistency, and the similarity computing approach of knowledge ontology was presented.

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