摘要翻译:
本文应用信息粒化理论,对魁北克东南墙杰弗里矿进行反分析。该方法将自组织映射(SOM)和粗糙集理论(RST)相结合,得到了粗粒和粗粒。脆粒和亚粗粒的平衡采用闭开迭代的方式进行。该方法将软硬计算,即有限差分法(FDM)与计算智能相结合,并考虑缺失信息,是该方法的两个主要优点。作为实例,对东南壁杰弗里矿的破坏进行了反演分析。
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英文标题:
《Back analysis based on SOM-RST system》
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作者:
H. Owladeghaffari, H. Aghababaei
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最新提交年份:
2009
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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 information granulation theory, on the back analysis of Jeffrey mine southeast wall Quebec. In this manner, using a combining of Self Organizing Map (SOM) and rough set theory (RST), crisp and rough granules are obtained. Balancing of crisp granules and sub rough granules is rendered in close-open iteration. Combining of hard and soft computing, namely finite difference method (FDM) and computational intelligence and taking in to account missing information are two main benefits of the proposed method. As a practical example, reverse analysis on the failure of the southeast wall Jeffrey mine is accomplished.
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PDF链接:
https://arxiv.org/pdf/0909.2339