基于粗糙集和BP神经网络的供应链库存预警研究

2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI:10.1109/ICRMEM.2008.118

J. Hua, Ruan Jun-hu

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引用次数: 1

摘要

本文将粗糙集和人工神经网络相结合，对供应链的库存预警进行了分析。粗糙集的引入降低了人工神经网络的输入维数，并通过加入动量因子mc和自适应学习率对人工神经网络算法进行了改进。最后，以邯郸市某制造企业的库存数据为例，验证了所提模型的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Study on Inventory Early-Warning in Supply Chains Based on Rough Sets and BP Neural Network

The paper combines rough sets and ANN to analyze inventory early-warning in supply chains. The introduction of Rough sets cuts down the input dimensions of ANN, and the ANN algorithm is improved by adding the momentum factor mc and applying adaptive learning rate. Lastly, according to the inventory data of a manufacturing enterprise in Handan City, the paper proves the validity of the proposed model.

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2008 International Conference on Risk Management & Engineering Management

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