基于信息粒化理论的渗透率分析

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摘要翻译：
本文介绍了信息粒度理论在“路由数据”分析中的应用。采用自组织映射(SOM)和神经模糊推理系统(NFIS)相结合的方法，得到了清晰的模糊粒。在非模糊信息（初始粒）内，用开闭迭代的方法实现了脆粒和亚模糊粒的平衡。采用“规则的简单性”和“合适的自适应阈值误差水平”两个准则，保证了算法的稳定性。本文的其他部分采用粗糙集理论(RST)进行近似分析，并在伊朗Shivashan大坝岩体原位渗透率大数据集上进行了验证。通过在lugeon数据集上的实现，证明了本文提出的渗透率近似分析方法是可行的。
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英文标题：
《Permeability Analysis based on information granulation theory》
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作者：
M.Sharifzadeh, H.Owladeghaffari, K.Shahriar, E.Bakhtavar
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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：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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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 analysis of "lugeon data". In this manner, using a combining of Self Organizing Map (SOM) and Neuro-Fuzzy Inference System (NFIS), crisp and fuzzy granules are obtained. Balancing of crisp granules and sub- fuzzy granules, within non fuzzy information (initial granulation), is rendered in open-close iteration. Using two criteria, "simplicity of rules "and "suitable adaptive threshold error level", stability of algorithm is guaranteed. In other part of paper, rough set theory (RST), to approximate analysis, has been employed >.Validation of the proposed methods, on the large data set of in-situ permeability in rock masses, in the Shivashan dam, Iran, has been highlighted. By the implementation of the proposed algorithm on the lugeon data set, was proved the suggested method, relating the approximate analysis on the permeability, could be applied.
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PDF链接：
https://arxiv.org/pdf/0804.0352

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关键词：渗透率 Evolutionary Intelligence Presentation information proposed 准则 been crisp 岩体