计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (30): 242-244.DOI: 10.3778/j.issn.1002-8331.2009.30.072

• 工程与应用 • 上一篇    下一篇

人工神经网络在径流影响因子滞后性研究中的应用

冉笃奎1,2,李 敏3,武 晟1,4,解建仓1   

  1. 1.西安理工大学 西北水资源与环境生态教育部重点实验室,西安 710048
    2.汉江水电开发有限责任公司,湖北 丹江口 442700
    3.西北勘测设计研究院,西安 710065
    4.西北电力设计研究院,西安 710075
  • 收稿日期:2008-06-12 修回日期:2008-08-01 出版日期:2009-10-21 发布日期:2009-10-21
  • 通讯作者: 冉笃奎

Application of ANN in study of hysteretic nature of factors influencing inflow runoff

RAN Du-kui1,2,LI Min3,WU Sheng1,4,XIE Jian-cang1   

  1. 1.Key Lab of Northwest Water Resources and Environment Ecology of MOE,Xi’an University of Technology,Xi’an 710048,China
    2.Hanjiang Hydropower Development Co.,Ltd,Danjiangkou,Hubei 442700,China
    3.Northwest Investigation and Design Institute,Xi’an 710065,China
    4.Northwest Electric Power Design Institute,Xi’an 710075,China
  • Received:2008-06-12 Revised:2008-08-01 Online:2009-10-21 Published:2009-10-21
  • Contact: RAN Du-kui

摘要: 根据径流量的影响因素往往具有滞后性的特点,在构建神经网络时,从模型复杂度、训练精度、预测精度等方面综合分析了该特性的影响大小,获得了分析滞后性影响的方法。实例表明该方法能够准确判断出滞后时段的大小,为提高径流预报的准确性提供了一条有效的途径。

关键词: 人工神经网络, 入库径流, 滞后性

Abstract: The hysteretic nature of runoff’s influencing factors’ is considered,the influencing degree of the hysteretic nature is tested from aspects of complexity,training precision,prediction accuracy of model during structuring ANN model,then the method of analyzing hysteretic nature’s influence is advanced.The result of example study demonstrates that the lag effect time can been accurately predicted by the way,an effective way is given to improve the accuracy of runoff forecast.

Key words: Artificial Neural Network(ANN), inflow runoff, hysteretic nature

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