基于混合信息的结果扩张规则模糊推理算法及其应用  

A fuzzy reasoning consequent dilation rule algorithm based on mixed information and its application

作　　者：孙子阳 罗敏霞 SUN Ziyang;LUO Minxia(College of Sciences,China Jiliang University,Hangzhou 310018,China)

机构地区：[1]中国计量大学理学院,浙江杭州310018

出　　处：《中国计量大学学报》2024年第4期692-700,共9页Journal of China University of Metrology

基　　金：国家自然科学基金项目(No.12171445)。

摘　　要：目的:针对知识库规则与输入信息类型不匹配,研究基于混合信息的结果扩张规则(Consequent Dilation Rule,CDR)模糊推理算法。方法:通过分析混合了模糊集和区间值模糊集的多种FMP问题,构建不同类型的混合信息的分解型CDR算法和扩张型CDR算法的模型。结果:结合CDR算法,给出了不同类型的混合信息的CDR算法的模型解及其表现形式。结论:将基于混合信息的CDR算法应用在医疗诊断上,并与相似度方法进行比较,结果表明本文提出的方法是可靠的。Aims:This paper aims to study the mismatch between knowledge base rules and input information types.A mixed information based result expansion rule(Consequent Dilation Rule,CDR)was studied.Methods:By analyzing a variety of problems mixed with fuzzy sets and interval-valued fuzzy sets,the models of decomposition CDR algorithms and expansion CDR algorithms with different types of mixed information were constructed.Results:Combined with the CDR algorithm,the model solution and its expression form of the CDR algorithm of different types of mixed information were given.Conclusions:The CDR algorithm based on mixed information is applied to medical diagnosis and compared with the similarity method.The comparison results show that the method proposed in this paper is reliable.

关 键 词：混合信息 分解型CDR方法 扩张型CDR方法 

分 类 号：TB9[一般工业技术—计量学]