https://scholars.lib.ntu.edu.tw/handle/123456789/87216
標題: | 回饋式類神經網路於二階段即時流量預測 Devising a Two-Step Real-Time Recurrent Learning Algorithm for Streamflow Forecasting |
作者: | Chiang, Yen-Ming Chang, Li-Chiu Chang, Fi-John 江衍銘 張麗秋 張斐章 |
關鍵字: | 回饋式類神經網路;即時回饋學習演算法;線上學習;Recurrent neural network;Real-time recurrent learning algorithm;On-line learning | 公開日期: | 六月-2002 | 期: | 2 | 起(迄)頁: | 15-21 | 來源出版物: | 台灣水利 | 摘要: | The architecture of Recurrent Neural Network (RNN) provides an efficient representation of dynamic internal feedback loops in the system to store information for later use. The Real-Time Recurrent Learning (RTRL) algorithm deriving for RNN is an on-line learning. The main feature of the RTRL is that it doesn't need a great deal of historical data for training, and it can perform on-line learning with training and operating at the same time. Most of the traditional physical models or feed-forward neural networks for streamflow forecasting emphasize the one-step ahead for ecasting. Due to the high mountain and steep channel all over the Taiwan island, the peak flow usually approach downstream about 2 or 3 hours after heavy rainfall on the watershed. Consequently, it is desired and will be very beneficial if the model can provide multi-step ahead forecasting. The main aim of this study is to develop a two-step ahead algorithm based RTRL. For the purpose of comparison, the predictive ability of RTRL and ARMAX model are performed by using the rainfall-runoff data of the Da-Chia River. Our results demonstrate that the RTRL has a learning capacity with high efficiency and is an adequate model for time series prediction. |
URI: | http://ntur.lib.ntu.edu.tw//handle/246246/176198 |
顯示於: | 生物環境系統工程學系 |
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回饋式類神經網路於㆓階段即時流量預測.pdf | 539.38 kB | Adobe PDF | 檢視/開啟 |
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