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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/68039

    Title: 即時回饋式類神經網路於流量推估之應用
    Authors: 張斐章;黃浩倫;張麗秋;Chang, Li-chiu
    Contributors: 淡江大學水資源及環境工程學系
    Keywords: 回饋式神經網路;流量估計;動態神經元;即時學習演算法;大甲溪;Recurrent Neural Network;Flow Estimation;Dynamic Neuron;Real Time Recurrent Learning;Ta-Chia Stream
    Date: 1998-12-22
    Issue Date: 2011-10-23 09:39:39 (UTC+8)
    Publisher: 臺北市:中國農業工程學會
    Abstract: 回饋式神經網路(Recurrent neural network, RNN)藉由動態神經元(Dynamic neurons), 有效學習時間序列的前後關係, 並儲存早期的資訊留到以後使用。即時學習演算法(Real time recurrent learning)的特性是不需要有大量的歷史資料作為訓練範例, 能隨真實環境物理特性的改變作有效而迅速的學習。回饋式神經網路與即時學習演算法合併使用架構出來的模式用來作大甲溪上游流量的推估可以得到良好的結果, 顯示出即時回饋式神經網路的優越能力。
    This research presents an alternative approach of the Artificial Neural Network (ANN) model to estimate streamflow. The architecture of Recurrent Neural Network(RNN) that we used provides a representation of dynamic internal feedback loops in the system to store information for later use. The Real-Time Recurrent Learning (RTRL) algorithm is implanted to enhance the learning efficiency. The main feature of the RTRL is that it doesn't need a lot of historical examples for training. Combining the RNN and RTRL to model watershed rainfall-runoff process will complement traditional techniques in the streamflow estimation.
    Relation: 八十七年度農業工程研討會論文集,頁703-709
    Appears in Collections:[Graduate Institute & Department of Water Resources and Environmental Engineering] Proceeding

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