摘要
针对当前大坝安全监测中广泛采用的回归模型欠拟合的不足,提出了基于差异进化算法的前馈神经网络模型。差异进化算法是基于种群策略的全局优化搜索算法,具有应用简单、收敛快的优点。采用该法训练的神经网络可以有效避免常规BP(back propagation)神经网络收敛于局部极小点的缺陷。将提出的方法应用于某拱坝的变形监测,通过计算表明,应用DE(differential evotntion)神经网络模型预报大坝变形的精度比常规回归模型和BP神经网络模型均有所提高。
The model of feed-forward neural networks trained by differential evolution （DE） algorithm is presented to overcome the shortcoming of traditional regression model widely used in monitoring the safety and deformation of dams. DE algorithm is a population-based one in global optimization, with the merits of being easy to use and fast convergence. The neural networks trained by DE can effectively avoid the problem of being stuck in any local minimum that often happens in classical BP neural networks model. The case study of deformation monitoring of an arch dam shows that the DE neural networks model proposed results in a better precision, comparing with traditional regression model and BP neural networks model.
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2006年第4期597-600,共4页
Rock and Soil Mechanics
基金
中国博士后科学基金资助(No.20040350089)
关键词
大坝变形监测
差异进化算法
前馈神经网络
BP神经网络
回归模型
dam deformation monitoring
differential evolution algorithm
feed-forward neural networks
BP neural networks
regression model
