Adaptive Neural Network in Logistics Demand Forecasting

摘要：

Logistics demand forecasting is an important process between Logistics programming and Logistics resource allocation. The neural network algorithm is usually applied to forecasting logistics demand. However it has the problems of slow convergence and local optimization in searching results when the training data is excessive. This paper presents an adaptive neural network algorithm for logistics demand forecasting. The empirical study shows that the adaptive neural network algorithm has faster convergence and higher precision than neural network algorithm.

作者: Yin Yanlin Bu Xuhui Yu Fashan

作者单位: School of Electrical engineering and Automation Henan Polytechnic University, Jiaozuo, china Advanced Control Systems Lab., School of Electronics and Information Engineering, Beijing Jiaotong U

会议类型: 国际会议

会议名称: International Conference on Intelligent Computation Technology and Automation(2008 智能计算技术与自动化国际会议 ICICTA 2008)

会议地点: 长沙

会议语种:英文

页码: 168-172

在线出版日期: 2008-10-20（万方平台首次上网日期，不代表论文的发表时间）