Proceedings ArticleDOI
Rough set theory and its application in the intelligent systems
Wei Pan,Jinhui Yi,Ye San +2 more
- pp 3706-3711
TLDR
The basic ideas of rough set theory are introduced, and the notion of up and low approximation sets, attribute reduction, core and some extensions of roughSet theory are presented, which gives new ideas to solve the hard problems in intelligent control.Abstract:
The rough set theory is a new mathematical tool to study vague and uncertain information, and is widely used in intelligent systems. In this paper, the basic ideas of rough set theory are introduced, and the notion of up and low approximation sets, attribute reduction, core and some extensions of rough set theory are also presented. Then the application of rough set theory in intelligent systems. The combination of rough sets with fuzzy sets, neural network and genetic algorithms is mainly reviewed, which gives new ideas and methods to solve the hard problems in intelligent control.read more
Citations
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Journal ArticleDOI
Granular neural networks
TL;DR: This article summarizes the recent research development of FNNs and RNNs (together called granular neural networks), which aims to process the massive volume of uncertain information, which is widespread applied in the authors' life.
Rough Neural Networks: A Review
TL;DR: This article summarizes the recent research development of RNNs and discusses the neural networks based on rough set, which have the strongly fault tolerance, self-organization, massively parallel processing and self-adapted, and can process the massively and uncertainly information.
Journal ArticleDOI
Research and Development of Granular Neural Networks
Hui Li,Shifei Ding +1 more
TL;DR: The basic model of GrC is introduced: word calculation model based on fuzzy sets theory, rough sets model, granular computing modelbased on quotient space theory and so on, and the research progress of fuzzy neural networks and rough neural networks is summarized.
Book ChapterDOI
Mammogram Image Classification Using Rough Neural Network
TL;DR: RST is integrated with back-propagation network (BPN) to classify digital mammogram images and the experimental results show that the RNN performs better than BPN in terms of classification accuracy.
Proceedings ArticleDOI
Periodic statistical prediction adaptive memory incremental control
TL;DR: A statistical control method based on control micro-unit, which facilities programming on microprocessor for its high computing speed and high efficiency control of the simulation, is proposed.
References
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Journal ArticleDOI
Rough sets
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Journal ArticleDOI
Rough sets theory for multicriteria decision analysis
TL;DR: The original rough set approach proved to be very useful in dealing with inconsistency problems following from information granulation, but is failing when preference-orders of attribute domains (criteria) are to be taken into account and it cannot handle inconsistencies following from violation of the dominance principle.
Journal ArticleDOI
Rough set approach to incomplete information systems
TL;DR: This work proposes reduction of knowledge that eliminates only that information, which is not essential from the point of view of classification or decision making, and shows how to find decision rules directly from such an incomplete decision table.
Journal ArticleDOI
Relational interpretations of neighborhood operators and rough set approximation operators
TL;DR: This paper presents a framework for the formulation, interpretation, and comparison of neighborhood systems and rough set approximations using the more familiar notion of binary relations, and introduces a special class of neighborhood system, called 1-neighborhood systems.
BookDOI
Intelligent Decision Support
TL;DR: The paper presents the system LERS for rule induction, which computes lower and upper approximations of each concept and induces certain rules and possible rules that can be induced from the input data.