摘要
农业供应链金融可以有效促进节点企业之间的资金协作,对其进行快速精准的风险评价关系到农业供应链的高效发展以及农业生产的顺利进行。文章选取11个农业供应链金融业务的风险评价指标,构建基于GA(Genetic Algorithm)-BP神经网络(Back Propagation Neural Network)的农业供应链风险评价模型,并利用案例分析法对所提出的风险评估模型进行验证。结果表明,利用经遗传算法优化的BP神经网络评价农业供应链金融风险,不仅可以加快BP神经网络的收敛速度,同时可以避免BP神经网络容易产生局部最小的问题,提高预测速度和精度,可作为确定农业供应链风险的有效技术手段,为当前农业供应链各参与主体的金融风险管理提供有效的决策支持。
Agricultural supply chain finance can effectively promote the capital cooperation between nodal enterprises,and the rapid and accurate risk assessment is related to the efficient development of agricultural supply chain and the smooth progress of agricultural production.The risk evaluation model of agricultural supply chain based on Genetic Algorithm(GA)-Back Propagation Neural Network(BPNN)was established by selecting 11 risk evaluation indexes of agricultural supply chain financial business,and the risk evaluation model was verified by case analysis method.Results show that using the genetic algorithm to optimize the BP neural network evaluation of agricultural supply chain financial risk,not only can accelerate the convergence speed of BP neural network,and can avoid the BP neural network into a local minimum problem,improve the speed and precision of prediction can be used as determine the effective technical measures of agricultural supply chain risk,for the current agricultural supply chain each participation main body of financial risk management to provide effective decision support.
作者
孙中叶
徐晓燕
SUN Zhong-ye;XU Xiao-yan(School of Economic and Trade,Henan University of Technology,Zhengzhou Henan 450001,China;School of Finance and Economics,Zhengzhou Institute of Science and Technology,Zhengzhou Henan 450064,China)
出处
《技术经济与管理研究》
北大核心
2021年第8期78-82,共5页
Journal of Technical Economics & Management
基金
河南省高校哲学社会科学智库研究项目(2021-ZKYJ12)。
