基于人工神经网络的医疗器材需求预测
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R197.39

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A Study on Artificial Neural Networks-based Medical Equipment Demand Forecast
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    摘要:

    为揭示库存数据中的复杂关系进而降低库存水平,基于黑箱方法的思想,首先找出影响需求的各个因素,然后建立一个采用LM算法的BP神经网络初始预测模型,并用库存的历史数据进行网络训练,使影响需求的各个因素的内在联系的信息分散存储于权值矩阵W中,从而获得最终预测模型。利用此模型对大坪医院医疗器材进行需求预测,据此进行采购和库存管理,大大地降低了医院的库存成本,为医院库存系统的库存控制和管理决策提供了理论依据。

    Abstract:

    In order to uncover the complex relation in the inventory data and lower the inventory level, based on the idea of black box method, all factors that influence demand are identified firstly. Then an initial forecast model of BP neural networks that adopt LM algorithm is established, and is trained using hospital inventory's history data. The interrelation's information of each factor that influences demand is stored in the link weights matrix W dispersedly. The final forecast model is obtained. We use the model to forecast the demand of medical equipment in Daping hospital. Based on it, the inventory cost is reduced enormously. The theory for the inventory system can be used to make management decision.

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张俊才 黄万杰.基于人工神经网络的医疗器材需求预测[J].重庆大学学报,2003,26(10):156-158.

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  • 最后修改日期:2003-07-04
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