隐性知识外显案例匹配双维改进

Explicit case match of tacit knowledge based on two-dimensional improvement

隐性知识对知识应用与创新意义重大,为此设计一套隐性知识外显案例匹配算法。基于RS-PSO算法对案例匹配空间进行纵向压缩,得到最优条件属性集;采用聚合距离参数改进FCM算法,增强聚类效果并对案例匹配空间实施横向压缩;融合条件属性间关联特性以及知识用户主观偏好,引入Choquet模糊积分通过非线性规划模型求解案例视图;在此基础之上,考虑直觉模糊数自有信息量,兼顾相似度与关联度以确定匹配结果。算例结果表明,相较于传统匹配算法,其具有比较优势。

Tacit knowledge is of great significance to knowledge application and innovation.For this purpose,a set of tacit know-ledge explicit case matching algorithm was designed.The case matching space was compressed vertically based on RS-PSO algorithm to obtain the optimal condition attribute.The FCM algorithm was improved using the aggregation distance parameter to enhance the clustering effect and compress the case matching space horizontally.The Choquet fuzzy integral was introduced to solve the case view through the nonlinear programming model,which fused the correlation between conditional attributes and the subjective preference of knowledge users.On this basis,considering the intrinsic information of intuitionistic fuzzy number,the similarity and relevancy,the matching result was received.Results of an illustration assert that compared with the traditional matching algorithm,it has a comparative advantage.