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| 1 | 基于BP神经网络的场次洪水河道沙量预报显示文摘针对日益严重的河流泥沙问题,掌握河流泥沙的影响因素和泥沙的变化过程是泥沙治理的关键。以神经网络模型为基础,建立场次洪水沙量预报模型,对多沙河流的洪水挟沙量进行预报,并取得较好的预报效果。选择辽西北多沙河流大凌河作为研究实例,首先将1984年-1998年间的29场历史实测洪水资料进行分析,得到影响下游沙量的主要因素;然后,通过神经网络模型建立上游影响因素与下游沙量之间的关系;最后,选取其中6场洪水资料进行验证。模型计算结果表明,计算结果与实测结果误差在合理范围之内,精度符合要求,可以用于下游沙量的预报。 | 何斌 张澎辉 梁国华 刘皓 | 2015 | 南水北调与水利科技2015，13，3： | 8 |
| 2 | 某型电源装备虚拟训练系统的综合评判模型显示文摘针对某型电源装备训练及业务能力不易准确评估的问题,以某型电源装备为具体研究对象,研究设计了适用于该电源装备的虚拟训练系统,提出一种基于组合赋权和模糊灰色聚类相结合的综合评价新方法。首先基于层次分析法(Analytical Hierarchy Process,AHP)、熵权法分别计算指标的权重,再通过组合赋权法将上述两种权重重新组合,消除主观和客观确定权重时所带来的不确定性因素影响。其次针对评价指标中包含的灰色信息,引入模糊灰色聚类算法,解决了模糊评判矩阵不易构建的问题。最后通过实例验证了该综合评判模型的有效性。 | 张琦 程培源 陆屹 | 2017 | 火力与指挥控制2017，42，12： | 1 |
| 3 | Evaluation of suspended load transport rate using transport formulas and arti- ficial neural network models (Case study: Chelchay Catchment)显示文摘Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient ( r = 0.69)and lower Root Mean Square Error (RMSE = 0.013) is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models including 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively. | HADDADCHI Arman MOVAHEDI Neshat VAHIDI Elham OMID Mohammad Hossein DEHGHANI Amir Ahmad | 2013 | Journal of Hydrodynamics2013，25，3： | 1 |
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