Adaptive Neural Network in Logistics Demand Forecasting

Abstract

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.