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A Prediction Model of Emergency Material Demand based on Case-Based Reasoning

机译:基于案例推理的应急材料需求预测模型

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In order to solve the problem how to determine the quantitative relationship of characteristic factors caused by uncertainties, this paper describes the target case by characteristic factors in case-based reasoning, determines the initial weight according to the characteristic factor matrix, introduces Hebb learning rule to make a weight adjustment and applies the improved Euclidean distance to calculate the similarity between the cases to look for the most similar case. The similarity comparison between CBR based on Grey Correlation and the proposed method proves this model a better performance. At last, the application example of the prediction model after an earthquake can prove the feasibility.
机译:为了解决问题如何确定不确定性引起的特征因素的定量关系,本文描述了在基于案例的推理中的特征因素来描述目标情况,根据特征因子矩阵确定初始重量,介绍了HEBB学习规则进行体重调整,并应用改进的欧几里德距离以计算案例之间的相似性,以查找最相似的情况。基于灰色相关的CBR与所提出的方法的相似性比较证明了这种模型的性能更好。最后,地震后预测模型的应用示例可以证明可行性。

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