数据与知识双驱动的备件需求模糊预测模型

针对知识驱动型需求预测模型所需的专家知识稀缺、数据驱动型需求预测模型可解释性不足的问题,提出了数据与知识双驱动的备件需求模糊预测模型。该模型基于模糊聚类算法将数值型数据聚类为结构简单、可解释性强的规则库,运用模糊逻辑将领域专家知识表示为Mamdani型规则库。在此基础上,引入了一种新型智能计算理论——模糊网络理论对两类规则库进行合并运算,形成初始预测模型。采用遗传算法优化模型规则库的模糊集参数来提高模型预测准确性。通过与模糊聚类算法进行对比,提出的模型在可解释性以及准确性指标上均具有优势。

Spare parts demand fuzzy prediction model driven by data and knowledge

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.