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Improved BP neural network-based back analysis of displacements

机译:基于改进的BP神经网络的位移反分析

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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.
机译:针对传统位移反分析过程中模型复杂,速度慢的问题,采用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,采用在神经网络中增加协调器的方法和归一化的方法来加快网络的训练速度。将实际测得的位移输入到训练后的BP网络中,以获得相应的力学参数,然后将其用作有限元计算的计算参数,并获得计算出的位移值。通过有限元分析计算出的位移值与实际测量值之间的差异非常小,最大误差不超过5%。结果表明,人工神经网络的模型建立和计算速度快,模型结构简单,精度高等优点,可用于工程中位移的反分析。

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