TY - JOUR
T1 - Identification of Surface Characteristics from Large Samples
AU - Kovacevic, R.
AU - Zhang, Y. M.
PY - 1992/7
Y1 - 1992/7
N2 - Surface roughness characteristics have been modelled by autoregressive moving average (ARMA) models. Frequently, extra-large samples from the surface are available. Due to the non-linearity and the computational burden dependence on sample size, the available data can not be sufficiently utilized to fit ARMA models in most cases. In an attempt to sufficiently employ the available data, an innovative ARMA identification approach is presented. The computational burden of this approach is nearly independent of the sample size. The accuracy ratio between the present approach and the non-linear least squares algorithm is determined. Both simulation and application have been conducted to confirm its effectiveness.
AB - Surface roughness characteristics have been modelled by autoregressive moving average (ARMA) models. Frequently, extra-large samples from the surface are available. Due to the non-linearity and the computational burden dependence on sample size, the available data can not be sufficiently utilized to fit ARMA models in most cases. In an attempt to sufficiently employ the available data, an innovative ARMA identification approach is presented. The computational burden of this approach is nearly independent of the sample size. The accuracy ratio between the present approach and the non-linear least squares algorithm is determined. Both simulation and application have been conducted to confirm its effectiveness.
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U2 - 10.1243/PIME_PROC_1992_206_127_02
DO - 10.1243/PIME_PROC_1992_206_127_02
M3 - Article
AN - SCOPUS:0026995584
SN - 0954-4062
VL - 206
SP - 275
EP - 284
JO - Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
JF - Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
IS - 4
ER -