%0 Journal Article
%T IDENTIFICATION OF PERMEABILITY COEFFICIENT OF ROCK MASS IN DAM FOUNDATION BASED ON GENETIC NEURAL NETWORK<br>基于遗传神经网络的坝基岩体渗透系数识别
%A He Xiang
%A Li Shouju
%A Liu Yingxi
%A Zhou Yuanpai
%A <br>何翔
%A 李守巨
%A 刘迎曦
%A 周园π
%J 岩石力学与工程学报
%D 2004
%I 
%X 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.
%K genetic neural network
%K permeability coefficient
%K parameter identification
%K global optimum
%K normalization procedure<br>坝基
%K 岩体渗透
%K 遗传神经网络
%K 数学模型
%K 地下水运动
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=D3421FAA1A0A0F0C&jid=35E747D791A1346351AFB523B7FA35CB&aid=C637956FDBB62DAC&yid=D0E58B75BFD8E51C&vid=EA389574707BDED3&iid=94C357A881DFC066&sid=8A6D8366EA57F0FF&eid=A8E9231F98774741&journal_id=1000-6915&journal_name=岩石力学与工程学报&referenced_num=12&reference_num=27