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Received:May 08, 2013 Revised:May 30, 2013
Received:May 08, 2013 Revised:May 30, 2013
中文摘要: 本文以四川省达州市州河流域的洪水为研究对象，分别采用标准BP算法、Levenberg- Marquart算法和遗传算法来建立洪水预报模型，并对预报结果进行了分析和比较. 结果表明：三种算法之中，遗传算法所建立的模型的收敛速度最快，预测结果精度最高，能够避免网络陷入局部极小点.
中文关键词: 神经网络 BP算法 Levenberg-Marquart算法 遗传算法 洪水预报
Abstract:By establishing flood forecasting models based on the standard BP algorithm, the Levenberg-Marquart algorithm and the genetic algorithm respectively, the paper studies the floods of the Zhouhe River in Dazhou city of Sichuan province, and analyzes and compares the outcomes of these three models. It shows that the model based on the genetic algorithm has the fastest convergence rate and highest accuracy in flood forecasting and can effectively prevent the network from getting into local minimum point.
keywords: neural network BP algorithm Levenberg-Marquart algorithm genetic algorithm flood forecasting
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基金项目:四川省教育厅2011年面上项目（11ZB139）；达州市2011年科技攻关项目（JCY1117）
引用文本：
侯翔,汤元斌,刘笃晋,江芝蒙.三种神经网络在洪水预报中应用的比较.计算机系统应用,2013,22(12):35-38
HOU Xiang,TANG Yuan-Bin,LIU Du-Jin,JIANG Zhi-Meng.Comparative Study on the Applications of Three Neural Networks to Flood Forecasting.COMPUTER SYSTEMS APPLICATIONS,2013,22(12):35-38
侯翔,汤元斌,刘笃晋,江芝蒙.三种神经网络在洪水预报中应用的比较.计算机系统应用,2013,22(12):35-38
HOU Xiang,TANG Yuan-Bin,LIU Du-Jin,JIANG Zhi-Meng.Comparative Study on the Applications of Three Neural Networks to Flood Forecasting.COMPUTER SYSTEMS APPLICATIONS,2013,22(12):35-38
