English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 57525/91039 (63%)
造访人次 : 13507028      在线人数 : 402
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/93124

    题名: Reinforced Recurrent Neural Networks for Multi-Step-Ahead Flood Forecasts
    作者: Chen, Pin-An;Chang, Li-Chiu;Chang, Fi-John
    贡献者: 淡江大學水資源及環境工程學系
    关键词: Reinforced real-time recurrent learning (R-RTRL) algorithm;Recurrent neural network (RNN);Multi-step-ahead forecast;Flood forecast
    日期: 2013-08-01
    上传时间: 2013-11-22 12:20:05 (UTC+8)
    出版者: Amsterdam: Elsevier BV
    摘要: Considering true values cannot be available at every time step in an online learning algorithm for multi-step-ahead (MSA) forecasts, a MSA reinforced real-time recurrent learning algorithm for recurrent neural networks (R-RTRL NN) is proposed. The main merit of the proposed method is to repeatedly adjust model parameters with the current information including the latest observed values and model’s outputs to enhance the reliability and the forecast accuracy of the proposed method. The sequential formulation of the R-RTRL NN is derived. To demonstrate its reliability and effectiveness, the proposed R-RTRL NN is implemented to make 2-, 4- and 6-step-ahead forecasts in a famous benchmark chaotic time series and a reservoir flood inflow series in North Taiwan. For comparison purpose, three comparative neural networks (two dynamic and one static neural networks) were performed. Numerical and experimental results indicate that the R-RTRL NN not only achieves superior performance to comparative networks but significantly improves the precision of MSA forecasts for both chaotic time series and reservoir inflow case during typhoon events with effective mitigation in the time-lag problem.
    關聯: Journal of Hydrology 497, pp.71-79
    DOI: 10.1016/j.jhydrol.2013.05.038
    显示于类别:[水資源及環境工程學系暨研究所] 期刊論文


    档案 描述 大小格式浏览次数
    reinforceRTRL-multistep_JH.pdf1072KbAdobe PDF565检视/开启



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈