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首页> 外文期刊>International Journal of Machine Tools & Manufacture: Design, research and application >Fuzzy similarity-based rough set method for case-based reasoning and its application in tool selection
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Fuzzy similarity-based rough set method for case-based reasoning and its application in tool selection

机译：基于模糊相似度的基于案例推理的粗糙集方法及其在工具选择中的应用

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摘要

Case-based reasoning (CBR) embodied in die and mold NC machining will extend the application of knowledge-based system by utilizing previous cases and experience. However, redundant features may not only dramatically increase the case memory, but also make the case retrieval algorithm more complicated. Additionally, traditional methods of feature weighting limit the development of CBR methodology. This paper presents a novel methodology to apply fuzzy similarity-based Rough Set algorithm in feature weighting and reduction for CBR system. The algorithm is used in tool selection for die and mold NC machining. The proposed method does not need to discretize continuous or real-valued features included in cases, from which can effectively reduce information loss. The weight of feature a{sub}i is computed based on the difference of its dependency defined as γ{sub}A - γ {sub}A-{a{sub}i} which also represents the significance of the corresponding feature. If the difference is equal to 0, the feature is considered to be redundant and should be removed. Finally, a case study is also implemented to prove the proposed method.
机译：模具数控加工中包含的基于案例的推理（CBR）将通过利用先前的案例和经验来扩展基于知识的系统的应用。但是，冗余功能不仅会大大增加案例存储空间，而且会使案例检索算法更加复杂。此外，传统的特征加权方法限制了CBR方法的发展。本文提出了一种基于模糊相似度的粗糙集算法在CBR系统特征加权和约简中的新方法。该算法用于模具NC加工的刀具选择。所提出的方法不需要离散化包含在案例中的连续或实值特征，从而可以有效地减少信息丢失。特征a {sub} i的权重是根据定义为γ{sub} A-γ{sub} A- {a {sub} i}的依存关系的差来计算的，这也代表了相应特征的重要性。如果该差等于0，则认为该要素是多余的，应将其删除。最后，还通过案例研究证明了该方法的有效性。

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