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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wen-Pai Wang Chun-Chih Chiu |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Industrial Engineering & Management, National Chin-Yi University of Technology, Taichung, Taiwan, R.O.C. (Wen-Pai Wang; Chun-Chih Chiu) |
| Abstract | Because of globalization, fast changes of technology and short life cycle of products, enhancing the accuracy of demand forecasts becomes one of the important issues for managers. The objective of this paper is to analyze and explore given data of orders using adaptive neuro-fuzzy inference system (ANFIS) and to draw up, by ANFIS learning mechanism, the relational rules from historical order data, whereby to construct the needed forecasting model, hoping to make accurate forecasts according to the demand variability. Afterward the proposed forecasting model is compared with the conventional regression analysis and back-propagation network to verify its feasibility and validity. |
| Starting Page | 1688 |
| Ending Page | 1692 |
| File Size | 667084 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424485017 |
| ISSN | 2157362X |
| e-ISBN | 9781424485031 |
| e-ISBN | 9781424485024 |
| DOI | 10.1109/IEEM.2010.5674595 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-07 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Accuracy Artificial neural networks demand variability Predictive models Data models Marketing and sales ANFIS Forecasting |
| Content Type | Text |
| Resource Type | Article |
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