Name
Abstract | ||
|---|---|---|
In this paper, we proposed and trained a fuzzy neural network system to estimate future logistics demand. The structure of neural network in the system is similar to that of BP network, except that here the nonlinear sigmoid functions in the networks are 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. |
Year | Venue | Keywords |
|---|---|---|
2004 | SHAPING BUSINESS STRATEGY IN A NETWORKED WORLD, VOLS 1 AND 2, PROCEEDINGS | logistics demand,forecasting,fuzzy logic,wavelet neural networks |
Field | DocType | Citations |
Convergence (routing),Neuro-fuzzy,Function approximation,Demand forecasting,Time delay neural network,Artificial intelligence,Engineering,Adaptive neuro fuzzy inference system,Artificial neural network,Sigmoid function | Conference | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianyu Zhang | 1 | 0 | 2.03 |
| Xuelei Pu | 2 | 0 | 0.34 |
| Sheng Li | 3 | 0 | 0.68 |
| Dan Yang | 4 | 56 | 10.48 |