| 基于遗传神经网络的坝基岩体渗透系数识别 |
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| 引用本文： | 何翔,李守巨,刘迎曦,周园π.基于遗传神经网络的坝基岩体渗透系数识别[J].岩石力学与工程学报,2004,23(5):751-757. |
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| 作者姓名： | 何翔 李守巨 刘迎曦 周园π |
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| 作者单位： | 大连理工大学工业装备结构分析国家重点实验室,大连,116023 |
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| 基金项目： | 国家自然科学基金(10072014)资助项目。 |
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| 摘 要： | 基于坝基岩体渗流场正演分析的数学模型，通过观测渗流区域地下水运动的动态信息反演坝基岩体的渗透系数。将遗传算法和神经网络相结合，所建立的遗传神经网络具有较快的训练速度和较强的泛化能力。数值算例表明，遗传神经网络在求解坝基岩体渗透系数反演问题中具有较高的计算效率和识别精度。
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| 关 键 词： | 坝基 岩体渗透 遗传神经网络 数学模型 地下水运动 |
| 文章编号： | 1000-6915(2004)05-0751-07 |
| 收稿时间： | 2002-4-22 |
| 修稿时间： | 2002-6-18 |
IDENTIFICATION OF PERMEABILITY COEFFICIENT OF ROCK MASS IN DAM FOUNDATION BASED ON GENETIC NEURAL NETWORK |
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| He Xiang,Li Shouju,Liu Yingxi,Zhou Yuanpai.IDENTIFICATION OF PERMEABILITY COEFFICIENT OF ROCK MASS IN DAM FOUNDATION BASED ON GENETIC NEURAL NETWORK[J].Chinese Journal of Rock Mechanics and Engineering,2004,23(5):751-757. |
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| Authors: | He Xiang Li Shouju Liu Yingxi Zhou Yuanpai |
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| Abstract: | The mathematical model of seepage field is introduced as the basis in identifying the permeability coefficient of the dam foundation by observing the dynamic information of the movement of underground water in seepage field. With the combination of genetic algorithm with artificial neural network,the newly established genetic neural network is of faster training speed and superior generating ability. The presented numerical example shows that the genetic neural network is of both higher computing efficiency and higher identification accuracy. |
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| Keywords: | genetic neural network permeability coefficient parameter identification global optimum normalization procedure |
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