国防科技大学学报2024，Vol.46Issue(2)：205-214,10.DOI:10.11887/j.cn.202402021
数据与知识双驱动的备件需求模糊预测模型
Spare parts demand fuzzy prediction model driven by data and knowledge
摘要
Abstract
Aiming at the problem of scarcity of expert knowledge required by knowledge-driven demand forecasting model and insufficient interpretability of data-driven demand forecasting model,a fuzzy prediction model of spare parts demand driven by data and knowledge was proposed.Based on the fuzzy clustering algorithm,the numerical data was clustered into a rule base with simple structure and strong interpretability.The domain expert knowledge was represented as a Mamdani-type rule base by utilizing fuzzy logic.On this basis,a new type of intelligent computing theory—fuzzy network theory was introduced,the two types of rule bases were merged into an initial prediction model.A genetic algorithm was employed to optimize the fuzzy set parameters of the model's rule base to enhance the model's predictive accuracy.Compared with the fuzzy clustering algorithm,the proposed model has advantages in interpretability and accuracy.关键词
预测模型/备件/模糊网络/遗传算法Key words
prediction model/spare parts/fuzzy network/genetic algorithm分类
自科综合引用本文复制引用
王小巍,陈砚桥,金家善,魏曙寰..数据与知识双驱动的备件需求模糊预测模型[J].国防科技大学学报,2024,46(2):205-214,10.基金项目
国家部委基金资助项目(LJ20191A020110) （LJ20191A020110）
