Title :
Least squares best fit using linear prediction for engineering surfaces metrology
Author_Institution :
Lab. d´´Electr., Signaux et Robotique, Ecole Normale Superieure de Cachan, France
Abstract :
This paper sets out applications of a method for fitting a deterministic model M to a data set with a least squares criterion. Current least squares methods fit M to the data set only, ignoring all other points of the surface. The present method applies linear prediction of a random function, taking into account the whole surface by estimating an optimal reference surface with respect to which the estimated R.M.S. is minimized
Keywords :
deterministic algorithms; engineering computing; least squares approximations; prediction theory; random processes; spatial variables measurement; surface topography measurement; applications; current least squares; deterministic model; engineering surfaces metrology; fitting; industrial measurement; least squares best fit; least squares criterion; linear prediction; milled surfaces; optimal reference surface; random function; Equations; Genetic expression; Least squares methods; Medical services; Metrology; Parameter estimation; Polynomials; Predictive models; Reactive power; Surface fitting;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 1996. IMTC-96. Conference Proceedings. Quality Measurements: The Indispensable Bridge between Theory and Reality., IEEE
Conference_Location :
Brussels
Print_ISBN :
0-7803-3312-8
DOI :
10.1109/IMTC.1996.507421
