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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/80119

    Title: Reinforced Two-Step-Ahead Weight Adjustment Technique for Online Training of Recurrent Neural Networks
    Authors: Chang, Li-Chiu;Chen, Pin-An;Chang, Fi-John
    Contributors: 淡江大學水資源及環境工程學系
    Keywords: Real-time recurrent learning (RTRL) algorithm, recurrent neural network (RNN);streamflow forecast;time series forecast
    Date: 2012-08-01
    Issue Date: 2013-01-18 00:04:08 (UTC+8)
    Publisher: Piscataway: Institute of Electrical and Electronics Engineers
    Abstract: A reliable forecast of future events possesses great value. The main purpose of this paper is to propose an innovative learning technique for reinforcing the accuracy of two-step-ahead (2SA) forecasts. The real-time recurrent learning (RTRL) algorithm for recurrent neural networks (RNNs) can effectively model the dynamics of complex processes and has been used successfully in one-step-ahead forecasts for various time series. A reinforced RTRL algorithm for 2SA forecasts using RNNs is proposed in this paper, and its performance is investigated by two famous benchmark time series and a streamflow during flood events in Taiwan. Results demonstrate that the proposed reinforced 2SA RTRL algorithm for RNNs can adequately forecast the benchmark (theoretical) time series, significantly improve the accuracy of flood forecasts, and effectively reduce time-lag effects.
    Relation: IEEE Transactions on Neural Networks and Learning Systems 23(8), pp.1269-1278
    DOI: 10.1109/TNNLS.2012.2200695
    Appears in Collections:[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

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