基于人工蜂群算法与Elman神经网络的大坝变形监控模型
发布时间：2019-06-19 02:11
【摘要】：针对Elman神经网络收敛速度慢、容易陷入局部极小等问题,建立了人工蜂群算法(ABC)与Elman神经网络组合的大坝变形监控模型。应用于某混凝土重力坝的结果表明,单纯Elman神经网络建模方法预测的相对误差和标准差分别为3.50%和0.131,ABC-Elman(人工蜂群算法与Elman神经网络)模型预测的相对误差和标准差分别为1.98%和0.063。从各影响因子对大坝变形的贡献上看,水压分量占27.9%,温度分量占62.3%,时效分量占9.8%。ABC-Elman模型在建模效率、预测精度等方面均有一定的优势,较适合于大坝变形的建模分析,并可推广于大坝渗流、应力等监控模型中。
[Abstract]:In order to solve the problems of slow convergence and easy to fall into local minima of Elman neural network, a dam deformation monitoring model based on artificial bee swarm algorithm (ABC) and Elman neural network is established. The results applied to a concrete gravity dam show that the relative error and standard deviation of simple Elman neural network modeling method are 3.50% and 0.131, respectively, and the relative error and standard deviation of ABC 鈮，
本文编号：2501985
[Abstract]:In order to solve the problems of slow convergence and easy to fall into local minima of Elman neural network, a dam deformation monitoring model based on artificial bee swarm algorithm (ABC) and Elman neural network is established. The results applied to a concrete gravity dam show that the relative error and standard deviation of simple Elman neural network modeling method are 3.50% and 0.131, respectively, and the relative error and standard deviation of ABC 鈮，
本文编号：2501985
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