摘要
粗糙集的不确定性度量是粗糙集理论的重要研究内容之一。结合模糊理论和粒计算理论改进了粗糙集的不确定性度量方法。通过集合的相对知识粒度及边界熵给出了粗糙集的粗糙性度量函数与模糊性度量函数,随着近似空间知识粒的细分,粗糙集的粗糙度与模糊度均满足单调递减的性质。利用矩阵理论提出了易于实现的粗糙性度量与模糊性度量的矩阵算法。
As one of the most important issues in rough set theory,roughness and fuzziness of rough sets have been widely studied.An improved method is proposed for measuring the uncertainty of rough sets based on fuzzy theory and granular computing theory.A definition of relative knowledge granulation and a concept of boundary entropy for an information sys- tem are given, under which the measure functions of roughness and fuzziness are modified.Both of roughness and fuzziness are monotonously decreasing with the refining of knowledge granularities in approximation spaces.Two matrix algorithms are presented for measuring the roughness and fuzziness of rough sets ,which are easy to implement.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第29期42-45,共4页
Computer Engineering and Applications
基金
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60773174
No.60963006)
河北省自然科学基金(No.F2009000316)
关键词
粗糙集
不确定性
知识粒度
粗糙度
模糊度
rough sets
uncertainty
knowledge granularities
roughness
fuzziness
