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摘要翻译：
本文介绍了粗糙集理论在水力旋流器运行分析中的应用。该方法以自组织映射(SOM)作为预处理步骤，得到最清晰的数据颗粒。然后，利用SOM和粗糙集理论(RST)的结合--排序--从实验室测试中获得信息表上的主导规则。基于这些规则，实现了决策属性的近似估计。最后，将该方法与SOM-NFIS系统（简称SOFIS）作了简要的比较。
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英文标题：
《Application of Rough Set Theory to Analysis of Hydrocyclone Operation》
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作者：
H.Owladeghaffari, M.Ejtemaei, M.Irannajad
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最新提交年份：
2008
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
This paper describes application of rough set theory, on the analysis of hydrocyclone operation. In this manner, using Self Organizing Map (SOM) as preprocessing step, best crisp granules of data are obtained. Then, using a combining of SOM and rough set theory (RST)-called SORST-, the dominant rules on the information table, obtained from laboratory tests, are extracted. Based on these rules, an approximate estimation on decision attribute is fulfilled. Finally, a brief comparison of this method with the SOM-NFIS system (briefly SONFIS) is highlighted.
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PDF链接：
https://arxiv.org/pdf/0804.0528
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