摘要
针对传统的数学统计模型无法完全描述大坝变形量与多种荷载因素之间非线性映射关系的缺点,引入了一种基于遗传算法的小波神经网络模型,利用该模型对小波神经网络的初始权值、尺度因子进行全局优化搜索,克服了BP神经网络初始化的随机性以及网络易陷入局部极小值的不足,将该模型运用于大坝坝顶的径向、切向位移预测,结果表明,遗传算法优化的小波神经网络模型结构稳定性更好,预测精度较BP神经网络模型、小波神经网络模型有较大提高。
Aiming at the demerits of traditional mathematical statistical model couldn’t completely describe the nonlinear mapping relationship be-tween the dam displacement and various load factors,we introduced wavelet neural network model based on genetic algorithm to have a global opti-mization search for WNN’s initial weights and scale factor,the disadvantages that the net training might be easily fallen into local minimums and the BP neural network’s initialization was random could be avoided. This model was used in forecasting the radial displacement and tangential dis-placement of the dam,the results show that the genetic wavelet neural network’s structure stability is better,compared with BP neural network and wavelet neural network ,its prediction accuracy has improved greatly.
出处
《人民黄河》
CAS
北大核心
2014年第10期126-128,共3页
Yellow River
基金
国家自然科学基金资助项目(41071294)
广西"八桂学者"岗位专项经费资助项目
广西空间信息与测绘重点实验室资助课题(桂科能130511402
1207115-06)
广西矿冶与环境科学实验中心资助课题(KH2012ZD004)
广西研究生教育创新计划项目(YCSZ2014151)
关键词
大坝变形
遗传小波神经网络
BP
神经网络
小波神经网络
预测精度
dam displacement
genetic wavelet neural network
BP neural network
wavelet neural network
prediction accuracy
