Model Modification of the Mill Transmission System Based on the PSO-BP Neural Network
Tao Zheng
Bao Xianle
Guo Qintao
Zhou Tianyang
Abstract：Aiming at the complexity of the mill transmission system structure,the uncertainty of the con-straint conditions among the components and the nonlinearity,a finite element model correction method based on the PSO-BP neural network is proposed in this study.This method approximates the nonlinear mapping rela-tionship between the two by improving the back propagation(BP)neural network,combines with the actual struc-tural response,and uses the generalization property of the neural network to obtain the numerical value of the model design parameters.After the correction,the frequency error is reduced from a maximum of 18%to about 4%,and the error range of the correction coefficient is all within 0.5%,while obviously improving the accuracy of the finite element model.Meanwhile,it does not need a large number of iterative solving steps,avoids the com-plex nonlinear optimization process of the traditional inverse problem model modification method,improves the efficiency,verifies the feasibility of the PSO-BP neural network method applied to the transmission system of large mill,and lays a foundation for the overall analysis of the subsequent transmission system.
Keywords：Model modificationNeural networkModal analysisSimilar designHierarchical correction
Publication Date：2024-02-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：6( 48-53 )