• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >The promotion strategy of supply chain flexibility based on deep belief network
【24h】

The promotion strategy of supply chain flexibility based on deep belief network

机译：基于深度信仰网络的供应链灵活性推广策略

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Supply chain flexibility is the processing ability of the enterprize to deal with the uncertain environment of supply and demand. In this paper, we consider the supply side (node interrupt) and demand side (demand fluctuations) under uncertain environment. By using the deep belief network (DBN), which is composed of multilayer Restricted Boltzmann Machine (RBM), it establishes the supply chain flexibility network with optimization of the transfer node and flow. The deep belief network is trained by the data of large manufacturing enterprize, compared with the traditional neural network (MLR, BP and GA). The results show that the deep belief network model overcomes the shortcomings of the traditional neural networks, such as easy to fall into local optimum, long training time and low function fitting degree, and it has higher prediction accuracy. This network model based on the deep belief network can promote the supply chain flexibility more, when supply and demand fluctuations occur.
机译：供应链灵活性是企业的加工能力，处理不确定的供需环境。 在本文中，我们考虑在不确定环境下的供应方（节点中断）和需求侧（需求波动）。 通过使用由多层限制Boltzmann机（RBM）组成的深度信念网络（DBN），它建立了具有转移节点和流量的优化的供应链灵活性网络。 与传统的神经网络（MLR，BP和GA）相比，深度信仰网络受大型制造企业数据的培训。 结果表明，深度信仰网络模型克服了传统神经网络的缺点，如易于陷入局部最佳，长训练时间和低功能拟合度，并且具有更高的预测精度。 这种基于深度信仰网络的网络模型可以促进供应链的灵活性更多，当发生供需波动时更多。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号