摘要
针对BP人工神经网络具有易陷入局部极小等缺陷,提出了将遗传算法与神经网络结合,同时优化网络结构的权值与阈值的思想,建立了基于遗传算法的锚杆极限承载力预测的遗传神经网络模型。该模型以低应变动测的5个变量作为输入变量来对锚杆极限承载力进行预测,并与BP神经网络预测结果进行比较。数值算例表明,遗传神经网络在锚杆极限承载力预测中具有较高的计算效率和识别精度。
Due to some defects of BP neural network,the power size and the threshold value of the network structure are optimized by combining genetic algorithm with neural network. The estimation model of bolt bearing capacity is accordingly built based on the improved optimization algorithm. In this model, five low strain variables from dynamic testing are used to estimate the bolt bearing capacity. The calculated results are compared with those of BP neural network. The presented example shows that the genetic neural network is of both higher computing efficiency and higher identification accuracy in estimating the bolt bearing capacity.
出处
《工程地质学报》
CSCD
2006年第2期249-252,共4页
Journal of Engineering Geology
基金
教育部博士点特别研究基金(编号A50221)
国家自然科学研究基金(编号50379046)资助研究项目
关键词
遗传神经网络
锚杆
承载力
预测
Genetic neural network, Bolt, Bearing capacity, Prediction
