Method of Dynamic Fuzzy Symptom Vector in Intelligent Diagnosis
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摘要: 针对由诊断知识的增加带来的对智能诊断系统诊断征兆变化的需求与多方位征兆智能诊断中征兆单位、数值差距过大的问题,提出实时更新元素的动态模糊征兆向量方法。定义了动态模糊征兆向量的概念,采用本体论规范征兆向量元素,建立了基于本体论的征兆向量传输方式。通过分析故障诊断征兆的变化规律,建立了基于模糊隶属函数的诊断征兆模糊化处理方案。实例表明,动态模糊征兆向量方法能有效地解决智能诊断中征兆的更新与征兆数值、单位的统一问题。Abstract: Aiming at the requirement of diagnostic symptom real-time updating brought from diagnostic knowledge accumulation and great gap in unit and value of diagnostic symptom in multi parameters intelligent diagnosis,the method of dynamic fuzzy symptom vector is proposed.The concept of dynamic fuzzy symptom vector is defined.Ontology is used to specify the vector elements,and the vector transmission method based on ontology is built.The changing law of symptom value is analyzed and fuzzy normalization method based on fuzzy membership functions is built.An instance proved method of dynamic fussy symptom vector is efficient to solve the problems of symptom updating and unify of symptom value and unit.
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Key words:
- Intelligent diagnosis /
- Diagnostic symptom /
- Fuzzy membership function /
- Symptom vector /
- Ontology
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