期刊名称：Advance Journal of Food Science and Technology
印刷版ISSN：2042-4868
电子版ISSN：2042-4876
出版年度：2014
卷号：6
期号：5
页码：593-597
DOI：10.19026/ajfst.6.80
出版社：MAXWELL Science Publication
摘要：Demand prediction plays a key role in supply chain management of fresh agricultural products enterprises and its algorithm research is a hotspot for the researchers related. A new algorithm for demand prediction of supply chain management of fresh agricultural products is advanced based on BP neural network and immune genetic particle swarm optimization algorithm. First, the deficiencies of traditional BP demand prediction models are analyzed. Second, the BP neural network and immune genetic particle swarm optimization algorithm are integrated and some measures are taken to overcome the deficiencies of traditional BP demand prediction models and calculation flows of the presented algorithm are redesigned. Finally, the presented algorithm is realized with the data from certain fresh agricultural products supply chain and the experimental results verify that the new algorithm can improve effectiveness and validity of demand prediction for fresh agricultural products supply chain.
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