Because of the shortcomings of the classical rough sets model such as the sensitive to noise often encountered in many real - world applications , the dissertation presents a variable precision and md relation rough sets model from the perspective of rough membership function and micro - difference . not only can the vp - md model overcome the shortcomings of the classical model , but also is consistent with the statistics . this model can extend the application scopes of rough sets and enhance its adaptability 本文從對(duì)象的不可分辨關(guān)系出發(fā),討論了信息系統(tǒng)的經(jīng)典粗糙集模型,并針對(duì)經(jīng)典粗糙集模型存在的對(duì)噪音敏感等缺陷,提出了基于隸屬度和微差距離的可變精度微差關(guān)系( vp - md )模型,該模型不僅能夠處理含有噪音的不完全信息系統(tǒng),其結(jié)果也能反映大量數(shù)據(jù)所滿足的統(tǒng)計(jì)規(guī)律,使粗糙集理論的應(yīng)用范圍更廣、適應(yīng)性更強(qiáng)。
The dissertation suggests a series of algorithms to compute the sets and numeric data based on the vp - md model . to obtain the minimal reduction of attributes and the minimal reduction of values , the dissertation provides a csbark algorithm based on the context sensitivity ( cs ) of attributes and a minimal rules set algorithm based on value core 本文研究了基于vp - md模型計(jì)算粗糙集理論所涉及的數(shù)字量和集合量方法,并針對(duì)最小屬性約簡(jiǎn)和最小值約簡(jiǎn)這兩個(gè)np問(wèn)題提出了基于屬性上下文敏感度的啟發(fā)式屬性約簡(jiǎn)算法? csbark算法和基于值核的最小規(guī)則集求解算法。