专家系统中基于模糊对象匹配的研究

Fuzzy Object Match in Expert Systems

  • 摘要: 提出并研究一种新的知识表达模型“模糊对象”.它突破传统的精确模型, 是一条将模糊理论和面向对象技术应用于专家系统的途径. 详细分析了该模糊对象模型的意义、知识表达结构以及它在模糊专家系统中的推理机制. 并应用格贴近度和综合函数 γ- operator两种方法计算模糊对象与规则中各种模糊对象模式之间的匹配程度. 实验表明, 该方法能显著地提高信息的准确性和有效性. 最后探讨了用基于模糊专家系统外壳 Fuzzy CL IPS实现这一方法的可行性

     

    Abstract: Fuzzy object, a new model of knowledge representation, is proposed and investigated. This model breaks through the traditional crisp models and presents a new approach in applying fuzzy theory and object oriented technologies to expert systems. The significance, knowledge representation structure and reasoning mechanism of fuzzy object model are analyzed in detail. Two methods of similarity degree and intersection function are used to calculate the matching degree between fuzzy objects and different fuzzy object patterns in rules. Finally, the possibility of realizing the idea of fuzzy object based on FuzzyCLIPS, a fuzzy expert system shell, is discussed.

     

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