期刊論文
| 學年 | 92 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2004-01-01 |
| 作品名稱 | A two-step-ahead recurrent neural network for stream-flow forecasting |
| 作品名稱（其他語言） | |
| 著者 | 張麗秋; Chang, Li-chiu; Chang, F. J.; Chiang; Y. M |
| 單位 | 淡江大學水資源及環境工程學系 |
| 出版者 | Wiley Online |
| 著錄名稱、卷期、頁數 | Hydrological processes 18(1), pp.81-92 |
| 摘要 | In many engineering problems, such as flood warning systems, accurate multistep-ahead prediction is critically important. The main purpose of this study was to derive an algorithm for two-step-ahead forecasting based on a real-time recurrent learning (RTRL) neural network that has been demonstrated as best suited for real-time application in various problems. To evaluate the properties of the developed two-step-ahead RTRL algorithm, we first compared its predictive ability with least-square estimated autoregressive moving average with exogenous inputs (ARMAX) models on several synthetic time-series. Our results demonstrate that the developed two-step-ahead RTRL network has efficient ability to learn and has comparable accuracy for time-series prediction as the refitted ARMAX models. We then investigated the two-step-ahead RTRL network by using the rainfall–runoff data of the Da-Chia River in Taiwan. The results show that the developed algorithm can be successfully applied with high accuracy for two-step-ahead real-time stream-flow forecasting. Copyright © 2003 John Wiley & Sons, Ltd. |
| 關鍵字 | |
| 語言 | en |
| ISSN | |
| 期刊性質 | 國內 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/67794 ) |