文档介绍：基于神经网络的农产品物流需求预测研究1
王新利2 赵琨3
黑龙江八一农垦大学经济管理学院中国大庆 163319
摘要:农产品物流不仅具有一般性物流的共同特点,而且具有独特性和复杂性,这导致利用一般方
法进行农产品物流需求预测不仅难度大,而且精度差。为克服单项物流需求预测的局限性及农产品物流
数据的不完整性,利用神经网络理论,建立基于 BP 神经网络的农产品物流预测模型,并利用 BP 神经网
络在学习过程中据有工作信号正向传播和误差信号反向传播的特点,通过权值的不断修正,使网络的实
际输出向量更加接近期望的输出值,从而极大程度的提高了物流预测的准确性。经农产品物流预测实例
分析,验证了基于 BP 神经网络模型建立的物流需求预测模型的准确性与科学性。从而印证了人工神经
网络在物流领域中的应用具有更加广泛的发展潜力。
关键词: BP 神经网络;农产品物流量;预测
Study of agricultural product logistics: Demand prediction based on
work theory
WangXin-li Zhao kun
College of Economics & Management , HeiLongjiang August 1’st Land Reclamation University, Daqing ,
Heilongjing 163319 China
E-mail:xinli56@
Abstract: Agricultural product logistics shares the challenges of other logistical problems, but also
possesses many unique features which preclude the application of usual methods of the logistics
of agricultural products. In particular, it is not possible to accurately forecast demand. To
e the limitations of single logistics demand forecasting techniques and the difficulties in
agricultural products logistics that exist currently, this paper reports the use of work theory
to establish a predictive model of the demand in agricultural products logistics based on a
back-propagation (BP) work. The BP Algorithm used in the learning process includes
two processes: puting of data stream and backward propagation of error signals,
which