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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yin Yanling Bu Xuhui Yu Fashan |
| Copyright Year | 2008 |
| Description | Author affiliation: Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing (Bu Xuhui) || Sch. of Electr. Eng. & Autom., Henan Polytech. Univ., Jiaozuo (Yin Yanling; Yu Fashan) |
| 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. |
| Starting Page | 168 |
| Ending Page | 172 |
| File Size | 560745 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769533575 |
| DOI | 10.1109/ICICTA.2008.73 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-10-20 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Automation Demand forecasting forecasting Power generation economics Predictive models adaptive neural network Convergence Manufacturing industries Neural networks Economic forecasting Logistics demand Logistics |
| Content Type | Text |
| Resource Type | Article |
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