IDENTIFICATION OF SURFACE CHARACTERISTICS FROM LARGE SAMPLES

被引：2

作者：

KOVACEVIC, R ^{[1
]}

ZHANG, YM ^{[1
]}

机构：

[1] UNIV KENTUCKY,DEPT MECH ENGN,LEXINGTON,KY 40506

来源：

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE | 1992年 / 206卷 / 04期

关键词：

D O I：

10.1243/PIME_PROC_1992_206_127_02

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Surface roughness characteristics have been modelled by autoregressive moving averaqe (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.

引用

页码：275 / 284

页数：10