TY - JOUR
T1 - Modal parameters identification via an ANN-based method
AU - Fu, Zhichao
AU - Cheng, Wei
AU - Li, Jing
AU - Xu, Cheng
PY - 2010/12
Y1 - 2010/12
N2 - A novel artificial neural network (ANN)-based technique employing auto regression moving average (ARMA) time series method will be used to extract structural modal parameters. It takes advantage of the output only vibration signals to obtain the modal parameters. Firstly, the method uses ANN to calculate the unknown coefficients in ARMA form, and then, it extracts the modal parameters from the calculated matrix before. Both numerical evaluations and experimental results demonstrate that the ANN based method identifies structural modal parameter accurately and robustly. Furthermore, it has a more robust performance than the traditional time domain method. Thus the proposed method is very meaningful to the real measured vibration signals, and it is very suitable to extract the modal parameters from the real system.
AB - A novel artificial neural network (ANN)-based technique employing auto regression moving average (ARMA) time series method will be used to extract structural modal parameters. It takes advantage of the output only vibration signals to obtain the modal parameters. Firstly, the method uses ANN to calculate the unknown coefficients in ARMA form, and then, it extracts the modal parameters from the calculated matrix before. Both numerical evaluations and experimental results demonstrate that the ANN based method identifies structural modal parameter accurately and robustly. Furthermore, it has a more robust performance than the traditional time domain method. Thus the proposed method is very meaningful to the real measured vibration signals, and it is very suitable to extract the modal parameters from the real system.
KW - Artificial neural network
KW - Auto regression moving average model
KW - Ibrahim time domain method
KW - Identification
KW - Modal analysis
UR - https://www.scopus.com/pages/publications/78650538262
M3 - 文章
AN - SCOPUS:78650538262
SN - 1001-9669
VL - 32
SP - 899
EP - 904
JO - Jixie Qiangdu/Journal of Mechanical Strength
JF - Jixie Qiangdu/Journal of Mechanical Strength
IS - 6
ER -