Research on Application of Fuzzy Neural Networks for Logistics Forecasting
Jizi Li, Chunling Liu, Zhiping Zuo · 2008
In this paper, a fuzzy neural network system to estimate future logistics demand was proposed and trained. The structure of neural network in the system is different from BP network, with the nonlinear sigmoid functions in the networks replaced by fuzzy reasoning process and wavelet functions respectively. Moreover, the trained network system is put into practical logistics demand forecasting. The experimental results show that it has good properties such as a fast convergence, high precision and strong function approximation ability and is good at predicting future logistics amount.