| 洪泽湖水位预测模型的研究 |
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| 引用本文： | 朱全银,戴峻峰.洪泽湖水位预测模型的研究[J].计算机仿真,2009,26(4). |
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| 作者姓名： | 朱全银 戴峻峰 |
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| 作者单位： | 淮阴工学院计算机工程系,江苏,淮安,223001 |
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| 基金项目： | 江苏省高校自然科学基础研究面上项目 |
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| 摘 要： | 水位预测是进行洪水监测的规则非线性函数关系,不易使用某个函数进行逼近.采用了BP神经网络对历年的水文信息进行学习、建模,实现了对这种不规则函数的拟合,并支持在线学习及适时调整.另外,使用改进的粒子群优化算法(PSO)对常规的BP网络进行训练.实验结果表明使用由改进的粒子群优化算法进行训练的BP神经网络进行的水位预测的精度有显著提高,并且在训练过程中尽可能地避免收敛于局部最优值.
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| 关 键 词： | 水位预测 洪泽湖 神经网络 粒子群 |
A Water Level Prediction Model of Hongze Lake |
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| ZHU Quan-yin,DAI Jun-feng.A Water Level Prediction Model of Hongze Lake[J].Computer Simulation,2009,26(4). |
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| Authors: | ZHU Quan-yin DAI Jun-feng |
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| Affiliation: | Department of Computer Engineering;Huaiyin Institute of Technology;Huaian Jiangsu 223001;China |
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| Abstract: | The prediction of water level is the key to flood monitoring,and the surface area of Hongze Lake expands irregularly with the rising of water level.Therefore,the prediction of water level is corresponding to irregular nonlinear function,and is not easy to use a function approximation.In this article,BP neural network learning and modeling was used to achieve irregular function fitting.It supports the timely adjustment and online learning.In addition,the improved particle swarm optimization(IPSO) was used to... |
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| Keywords: | Prediction of water level Hongze Lake Neural networks PSO |
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