基于粗集理论的知识处理方法在专家系统中的应用

为了提高专家系统中知识获取的能力，本文提出了一种基于粗集理论的专家系统模型 ．该模型在知识获取阶段引入知识过滤器，根据知识依赖度的变化对采集的知识进行评价和 分类．该系统还在知识库构造阶段引入知识重构机制，对原有的知识库进行精简和重构．本 文提出的方法不仅消除了知识库中的冗余属性，还对属性值空间进行合理划分，对整个系统 的性能有明显的改善效果．

THE APPLICATION OF ROUGH-SET-BASED KNOWLEDGE PROCESSING METHOD IN EXPERT SYSTEM

To improve the capability of acquiring knowledge in expert system, in this paper we have proposed a rough set based expert system model. This model introduces a knowledge filter during the phase of knowledge acquisition. According to the variation of the knowledge dependency, it evaluates and classifies the newly collected knowledge. This system also introduces a mechanism for knowledge reconstruction during building knowledge base to refine and restructure the primitive knowledge base building. This method presented in the paper not only removes the redundant attributes of the knowledge base, but also restructures the value space of the attributes, and improves the performance of the whole system significantly.