| 二进制粒计算模型 |
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| 引用本文： | 郑鹭斌,陈玉明,曾志强,卢俊文.二进制粒计算模型[J].计算机科学,2016,43(1):270-274. |
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| 作者姓名： | 郑鹭斌 陈玉明 曾志强 卢俊文 |
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| 作者单位： | 厦门理工学院计算机与信息工程学院 厦门361024,厦门理工学院计算机与信息工程学院 厦门361024;江西省高性能计算重点实验室江西师范大学国家网络化支撑软件国际科技合作基地 南昌330027,厦门理工学院计算机与信息工程学院 厦门361024,厦门理工学院计算机与信息工程学院 厦门361024;江西省高性能计算重点实验室江西师范大学国家网络化支撑软件国际科技合作基地 南昌330027 |
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| 基金项目： | 本文受国家自然科学基金(61573297),福建省自然科学基金(2015J01277),江西师范大学国家网络化支撑软件国际科技合作基地开放课题(NSS1404,NSS1405)资助 |
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| 摘 要： | 粒计算是一种处理不确定性数据的理论方法,涵盖粗糙集、模糊集、商空间、词计算等。目前,数据的粒化与粒的计算主要涉及集合的运算与度量,集合运算的低效制约着粒计算相关算法的应用领域。为此,提出了一种二进制粒计算模型,给出了粒的三层结构,包括粒子、粒群与粒库,并定义了二进制粒子及二进制粒子的运算,将传统的集合运算转化为二进制数的计算,进一步给出了二进制粒子的距离度量,将等价类的集合表示方式转化为粒子的距离度量表示方式,给出了粒子距离的相关性质。该模型定义了二进制粒群距离的概念,给出了二进制粒群距离的计算方法,提出了基于二进制粒群距离的属性约简方法,证明了该方法与经典粗糙集约简方法的等价性,并以二进制粒群距离作为启发式信息,给出了两种约简算法。
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| 关 键 词： | 粒计算 粗糙集 二进制粒 粒群距离 |
| 收稿时间： | 2015/10/11 0:00:00 |
| 修稿时间： | 2015/11/11 0:00:00 |
Binary Granular Computing Model |
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| ZHENG Lu-bin,CHEN Yu-ming,ZENG Zhi-qiang and LU Jun-wen.Binary Granular Computing Model[J].Computer Science,2016,43(1):270-274. |
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| Authors: | ZHENG Lu-bin CHEN Yu-ming ZENG Zhi-qiang and LU Jun-wen |
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| Affiliation: | College of Computer and Information Engineering,Xiamen University of Technology,Xiamen 361024,China,College of Computer and Information Engineering,Xiamen University of Technology,Xiamen 361024,China;Key Laboratory of High Performance Computing Center of Jiangxi Province,Nanchang 330027,China,College of Computer and Information Engineering,Xiamen University of Technology,Xiamen 361024,China and College of Computer and Information Engineering,Xiamen University of Technology,Xiamen 361024,China;Key Laboratory of High Performance Computing Center of Jiangxi Province,Nanchang 330027,China |
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| Abstract: | Granular computing is a theory dealing with uncertain data,including rough set,fuzzy set,quotient space,computing with words,etc.At present,the granulation of data and granular computing are mainly related to the set ope-rations.As we know,these set operations are inefficient,resulting in restrict the applications of granular computing algorithms.Therefore,we proposed a binary granular computing model,which has the three layer structure including granule,granule swarm and granule library.We defined binary granules and granule operations,which can transform the set operations into the binary number calculations.Furthermore,we proposed a distance metric of two binary granules,which represents the distance of the set of equivalence classes,and discussed some properties of the granule distance.The binary granular computing model defines the concept of binary granule swarm distance,gives the calculation method of binary granule swarm distance,and puts forward the method of attribute reduction based on binary granule swarm distance.We proved the equivalence of our proposed reduction method and the classical Pawlak reduction method.We presented two kinds of reduction algorithm,which use the binary granule swarm distance as the heuristic information. |
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| Keywords: | Granular computing Rough sets Binary granules Granule swarm distance |
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