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首页> 外文期刊>Informatica: An International Journal of Computing and Informatics >Similarity measure of multiple sets and its application to pattern recognition
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Similarity measure of multiple sets and its application to pattern recognition

机译：多集的相似性测量及其应用于模式识别

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Multiple set is a newborn member of the family of generalized sets, which can model uncertainty together with multiplicity. It has the power to handle numerous uncertain features of objects in a multiple way. Multiple set theory has the edge over the well established fuzzy set theory by its capability to handle uncertainty and multiplicity simultaneously. Similarity measure of fuzzy sets is well addressed in literature and has found prominent applications in various domains. As multiple set is an efficient generalization of fuzzy set, the concept and theory of similarity measure can be extended to multiple set theory and can be developed probable applications in various real-life problems. This paper introduces the concept of similarity measure of multiple sets and proposes two different similarity measures of multiple sets and investigates their properties. Finally, this work substantiates application of the concept of similarity measure of multiple sets to pattern recognition. A numerical illustration demonstrates the effectiveness of the proposed technique to this application. Multiple set is a newborn member of the family of generalized sets, which can model uncertainty together with multiplicity. It has the power to handle numerous uncertain features of objects in a multiple way. Multiple set theory has the edge over the well established fuzzy set theory by its capability to handle uncertainty and multiplicity simultaneously. Similarity measure of fuzzy sets is well addressed in literature and has found prominent applications in various domains. As multiple set is an efficient generalization of fuzzy set, the concept and theory of similarity measure can be extended to multiple set theory and can be developed probable applications in various real-life problems. This paper introduces the concept of similarity measure of multiple sets and proposes two different similarity measures of multiple sets and investigates their properties. Finally, this work substantiates application of the concept of similarity measure of multiple sets to pattern recognition. A numerical illustration demonstrates the effectiveness of the proposed technique to this application.
机译：多个套装是普遍集体系列的新生成员，可以与多重性建模不确定性。它具有以多种方式处理众多不确定的物体的不确定特征。多个集合理论通过其能力同时处理不确定性和多重性的良好建立的模糊集理论。文学中的模糊集的相似性度量良好地解决了各个域中的突出应用。由于多个集是模糊集的有效概括，相似度测量的概念和理论可以扩展到多个集合理论，并且可以在各种现实生活中开发可能的应用。本文介绍了多集相似度测量的概念，提出了两个不同套件的不同相似度量，并调查其性质。最后，这项工作证实了多个集合测量的概念应用于模式识别。数字图示出了该应用的提出技术的有效性。多个套装是普遍集体系列的新生成员，可以与多重性建模不确定性。它具有以多种方式处理众多不确定的物体的不确定特征。多个集合理论通过其能力同时处理不确定性和多重性的良好建立的模糊集理论。文学中的模糊集的相似性度量良好地解决了各个域中的突出应用。由于多个集是模糊集的有效概括，相似度测量的概念和理论可以扩展到多个集合理论，并且可以在各种现实生活中开发可能的应用。本文介绍了多集相似度测量的概念，提出了两个不同套件的不同相似度量，并调查其性质。最后，这项工作证实了多个集合测量的概念应用于模式识别。数字图示出了该应用的提出技术的有效性。

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