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The information system classification is a crucial part of data mining, which aims to analysis the information system, extract important message from complex data, and forecast the future development trend of data. At present, there are many methods to classify the data, for example, Rough Set Theory, Decision Tree, Bayesian Network, Genetic Algorithm, etc. The method presented in this paper, based...
Attribute reduction is one of key processes in rough set theory. In this paper, a new attribute reduction algorithm and a definition of Attribute-Activity is proposed with theoretical basis. It uses Attribute-Activity to quantify the partition capability for an attribute and makes a rough sorting, then makes clustering analysis by calculating the similarity among attributes to modify the sorting to...
Fault diagnosis is the basic condition for smart grid to achieve the self-healing function, and it is also one of the important research topics of the intelligent dispatching decision support system. On the basis of analyzing its concept and aiming at the current status of China's studies, this paper reviewed and summarized several intelligent fault diagnosis methods, including expert system, artificial...
Considering the ability of rough sets theory on reduction of decision system and that of neural networks for clustering and nonlinear mapping, a new hybrid intelligent model of rough sets and neural networks for fault diagnosis is proposed. Meanwhile, a novel attribute reduction approach of rough set based on immune clonal selection is proposed, in order to find the minimal feature set of decision...
On the point view of complementary strategies, a new hybrid algorithm to optimize the RBF network based on artificial immunology was proposed. A dynamic clustering algorithm based on clonal selection algorithm was used to specify the amount and initial position of the RBF centers; then RBF network was trained by the immune evolutionary algorithm. Combining with the rough sets-based attribute reduction...
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