基于模糊聚类和直觉模糊推理的合成旅油料需求预测

需求预测是合成旅组织油料保障的基础环节，对合成旅成功遂行军事行动有着比较重要的影响.由于合成旅组成结构的特殊性，传统预测方法存在较大弊端，因此，提出了基于模糊聚类和直觉模糊推理的合成旅油料需求预测方法.首先，通过模糊C均值聚类算法实现对历史案例的初步筛选，以提高案例检索速度.然后，构建了案例特征属性的主客观综合权重模型和基于直觉模糊集的案例检索模型，保证了案例检索的准确度.最后，构建了基于整体数据特征的合成旅油料需求预测模型.通过算例分析验证上述预测方法的可行性和实用性，证明了该方法有助于提高检索速度和预测准确度.

Demand Forecast of POL for Synthetic Brigade Based on Fuzzy Clustering and Fuzzy Intuitionistic Reasoning

Demand forecasting is the basic link in the organization of POL support for synthetic brigades, which has a relatively important impact on the successful military operations of synthetic brigades. Because of the particularity of the composition structure of synthetic brigade, the traditional forecasting methods have some drawbacks. Therefore, a demand forecasting method for synthetic brigade based on fuzzy clustering and intuitionistic fuzzy reasoning is proposed. Firstly, the fuzzy C-means clustering algorithm is used to realize the preliminary screening of historical cases in order to improve the speed of case retrieval. Then, the subjective and objective comprehensive weight model of case feature attributes and the case retrieval model based on intuitionistic fuzzy sets are constructed to ensure the accuracy of case retrieval. Finally, a POL demand forecasting model for synthetic brigade based on the overall data characteristics is constructed. The feasibility and practicability of the forecasting method are verified by an example analysis, which proves that the proposed method is helpful to improve the retrieval speed and forecasting accuracy.