Self-organizing Map Method Based on Meal Rough Sets Space and Its Application of Pattern Recognition

XIAO Di; HU Shou-song

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Self-organizing Map Method Based on Meal Rough Sets Space and Its Application of Pattern Recognition

机译：Self-organizing Map Method Based on Meal Rough Sets Space and Its Application of Pattern Recognition

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摘要

This paper presents a real rough sets space and corresponding concepts of real lower and upper approximation sets which correspond to the real-valued attributes. Therefore, the real rough sets space can be investigated directly. A rhombus neighborhood for SOM is proposed, and the combination of SOM and rough sets theory is explored. According to the distance between the weight of winner node and the input vector in the real rough sets space, new weight learning rules are defined. The modified method makes the classification of the output of SOM clearer and the intervals of different classes larger. Finally, an example based on fault identification of an aircraft actuator is presented. The result of the simulation shows that this method is right and effective.

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来源
《Chinese Journal of Aeronautics》 |2006年第1期|72-82|共11页
作者
XIAO Di; HU Shou-song;
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作者单位

College of Automation 'Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;

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原文格式 PDF
正文语种英语
中图分类航空;
关键词
rough sets theory; self-organizing map; real value rough set; rhombus neighborhood; pattern recognition;

Self-organizing Map Method Based on Meal Rough Sets Space and Its Application of Pattern Recognition

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