基于灰色模糊推理的油料消耗预测

POL Consumption Forecast Based on Grey Relevance and Fuzzy Reasoning

摘要:

为了克服传统预测方法的弊端,提出了基于灰色模糊推理的油料消耗预测方法。首先,构建了基于加权灰色关联分析的案例检索模型,且运用信息熵理论确定灰色关联系数的权重；其次,构建了模糊集理论的案例检索模型,且运用改进的层次分析法确定特征属性的权重；最后,基于上述2种检索结果,运用灰色关联分析方法构建了组合检索模型,并且基于检索结果对油料消耗进行预测。通过算例仿真,证明了上述检索方法具有较高的准确度,验证了预测方法的可行性和实用性。

Abstract:

In order to overcome the drawbacks of traditional forecasting methods, a method of POL consumption forecasting based on weighted grey relational analysis and fuzzy case-based reasoning combined retrieval is proposed. Firstly, a case retrieval model based on weighted grey relational analysis is constructed, and the weight of grey relational coefficient is determined by using information entropy theory. Then, a case retrieval model based on fuzzy set theory is constructed, and the weight of feature attributes is determined by improved analytic hierarchy process. Finally, based on the above two retrieval models, a combined retrieval model is constructed by using grey relational analysis method, and the POL consumption is forecasted based on the retrieval results. A numerical example is given to demonstrate high accuracy of the retrieval method and the feasibility and practicability of the prediction method.