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    Abstract
2014 (Vol. 6, Issue: 5)
Article Information:

A New Algorithm for Demand Prediction of Fresh Agricultural Product Supply Chain

Xinwu Li
Corresponding Author:  Xinwu Li 

Key words:  BP neural network, demand prediction, fresh agricultural products, immune genetic particle swarm optimization algorithm, supply chain management, ,
Vol. 6 , (5): 593-597
Submitted Accepted Published
January 09, 2014 February 15, 2014 May 10, 2014
Abstract:

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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  Cite this Reference:
Xinwu Li, 2014. A New Algorithm for Demand Prediction of Fresh Agricultural Product Supply Chain.  Advance Journal of Food Science and Technology, 6(5): 593-597.
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ISSN (Online):  2042-4876
ISSN (Print):   2042-4868
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