基于改进BP神经网络的位移反分析

Zhang Guihua, Ma Xianmin, C. Jing
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引用次数: 1

摘要

针对传统位移反分析方法模型复杂、速度慢的问题,利用MATLAB的M语言编写了BP神经网络程序,用于位移反分析。针对传统BP神经网络收敛速度慢的缺点,采用在神经网络中加入协调器的方法和归一化方法来加快网络的训练速度。将实际测量的位移输入训练好的BP网络,得到相应的力学参数,作为有限元计算的计算参数,得到位移计算值。有限元分析计算的位移值与实际实测值相差很小,最大误差不超过5%。结果表明,人工神经网络方法具有模型建立和计算速度快、模型结构简洁、精度高等优点。它可用于工程中位移的反分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved BP neural network-based back analysis of displacements
For the problems of the complex model and the slow speed in the process of the traditional back analysis of displacements, the program of BP neural network is compiled by the M language of MATLAB and is used for the back analysis of displacements. Aimed at the disadvantage of slow convergence of the traditional BP neural network, the method of adding coordinator to neural network and the normalization method are used to quicken the network training rate. The practically measured displacements are input to the trained BP network to obtain the correspondent mechanics parameters, which are then used as the calculation parameters of the finite element calculation, and the calculated displacement values are got. The difference between the calculated displacement values by the finite element analysis and the practically measured values is very slight and the maximum error doesn't exceed 5%. It shows that the method of artificial neural network is fast in model building and calculation, brief in model structure , and high in precision etc. It can be used for back analysis of displacements in engineering.
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