System identification of dynamic structure by the multi-branch BPNN

Authors:
Hong-Nan Li;Hao Yang
Affiliations:
State Key Lab of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, PR China;Department of Civil Engineerng, Shenyang Jianzhu University, Shenyang, 110015, PR China
Venue:
Neurocomputing
Year:
2007

Citing 1
Cited 4

System identification: theory for the user

System identification: theory for the user

Seismic Responses Prediction of Nonlinear Building Structures Based on Multi-Branch BP Neural Network

ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Neural network based prediction schemes of the non-linear seismic response of 3D buildings

Advances in Engineering Software
Spatiotemporal prediction for nonlinear parabolic distributed parameter system using an artificial neural network trained by group search optimization

Neurocomputing
An adaptive neural network prediction for nonlinear parabolic distributed parameter system based on block-wise moving window technique

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

System identification is one of the critical factors to control structural vibration with high quality and evaluate whether control method can be applied or not. In this paper, a kind of multi-branch back propagation neural network (BPNN) model is proposed to identify a structural dynamic system. In this model, the primary factors that affect structural dynamic response, namely structure state variables and seismic inputs, are separately treated as the branches of the model, that is expected to enhance prediction precision. The aim of identification is to make the trained model be able to accurately predict structural future dynamic response. When the model is established, it can be trained with collected dynamic response and seismic wave data. In this paper, a numerical example is given. The analytic result turns out that the proposed identification model can accurately predict structural future dynamic response after being trained.