云映射和映射隶属云  

Cloud mapping and membership cloud of mapping

作　　者：杨志晓[1] 范艳峰[1] 

机构地区：[1]河南工业大学信息科学与工程学院,郑州450001

出　　处：《计算机应用研究》2012年第2期553-556,共4页Application Research of Computers

基　　金：河南省重点科技攻关计划资助项目(082102210096);河南省高校青年骨干教师计划资助项目(2009GGJS-056);郑州市科技局科技攻关计划资助项目(2010GYXM364)

摘　　要：为描述事物间关系的不确定性及其变化情况,提出云映射概念。对定义域的每个元素,通过随机变量将其非确定地变换到可能的取值空间,各随机变量的分布特征在定义域上构成特征函数,能够反映非确定变换随自变量的变化情况;提出了映射的隶属云概念,云映射在定义域全体元素的隶属云片构成映射的隶属云;采用映射的期望(即精确函数)、熵函数和超熵函数描述定性概念在整个自变量取值空间的变化情况,给出了正向映射云和逆向映射云算法。以人类记忆的遗忘过程为例,说明了云映射和映射隶属云的有效性及其应用前景。In order to describe uncertainties and their variation between events,this paper proposed a concept of cloud mapping.For each element of definition domain,used a matched stochastic variable to convert it to a possible value space.Distribution features of the stochastic variable cluster in definition domain form feature functions,which indicated variation of uncertainties according to change of independent variable.This paper proposed a concept of membership cloud of mapping.Each element of definition domain has a cloud piece.All cloud pieces form membership cloud of mapping.Expected function,used entropy function,and hyper entropy function to describe variation of qualitative concept in the whole definition domain.This paper proposed algorithms for generating positive mapping cloud and reverse mapping cloud.Taking human memory law as an example,introduced effectiveness and possible application of proposed cloud mapping and mapping cloud.

关 键 词：云映射 映射隶属云 模糊性 随机性 遗忘规律 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]