基于FCM-PFS与PSO-RF的隐性知识外显案例适配研究

Research on Adaptation of Tacit Knowledge Explicit Cases Based on FCM-PFS and PSO-RF

摘要:

对隐性知识外显案例实施有效适配，于知识应用乃至创新、实现乃至增值知识资源的价值，具有重要意义。本文深入研究了隐性知识外显案例适配机理。首先，采用毕达哥拉斯模糊集对知识属性值进行处理，建立知识表达系统；接着，运用K-Means算法对FCM聚类算法进行改进，压缩匹配空间、提升案例匹配效率；而后，基于PFS相关系数求解知识供需间的视图相似度，从而获得适配案例集。在此基础上，构建随机森林适配模型，并采用粒子群算法对其优化，以确保适配效果。与传统算法的对比实验验证了本文算法的比较优势。

Abstract:

Effective adaptation of tacit knowledge explicit cases is of great significance to knowledge application and innovation, realization and even value-added knowledge resources. This paper deeply studies the explicit case adaptation mechanism of tacit knowledge. First, the Pythagorean fuzzy set is used to process the knowledge attribute values to establish a knowledge expression system; then, the K-Means algorithm is used to improve the FCM clustering algorithm to compress the matching space and improve the case matching efficiency; then, based on the PFS correlation coefficient solves the view similarity between knowledge supply and demand, so as to obtain the adaptation case set. On this basis, a random forest adaptation model is constructed, and the particle swarm algorithm is used to optimize it to ensure the adaptation effect. The comparative experiment with the traditional algorithm verifies the comparative advantage of the algorithm in this paper.