Fitting transducer characteristics to measured data

Author

Dias Pereira, J.M. ; Silva Girão, P. M B ; Postolache, Octavian

Volume

4

Issue

4

fYear

2001

fDate

12/1/2001 12:00:00 AM

Firstpage

26

Lastpage

39

Abstract

There are no rules to select the best curve-fitting method for a given set of data. This problem is of great importance in measurement applications. Optimizing analog and digital methods for a transducer´s characteristic interpolation or linearization is a field where constant research is being done, particularly since auto-calibration and self-test of intelligent transducers is a topic of major interest. We present an overview of classical methods for data interpolation and least mean squares regression. We make a comparative evaluation of the relative performance of polynomial and artificial neural networks approximations to measurement data with particular attention paid to the reduction of the required calibration set dimension to obtain a given accuracy

Keywords

Newton method; calibration; curve fitting; function approximation; intelligent sensors; interpolation; least mean squares methods; neural nets; polynomial approximation; splines (mathematics); transducers; virtual instrumentation; Lagrange interpolation; Newton interpolation; artificial neural networks; calibration set dimension; curve-fitting method; data accuracy; data interpolation; function approximation; global interpolators; gradient methods; intelligent transducers; least mean squares regression; local interpolators; measurement data; nonlinear optical displacement sensor; polynomial approximations; relative performance; splines; Artificial intelligence; Artificial neural networks; Built-in self-test; Curve fitting; Interpolation; Least squares approximation; Optimization methods; Particle measurements; Polynomials; Transducers;

fLanguage

English

Journal_Title

Instrumentation & Measurement Magazine, IEEE

Publisher

ieee

ISSN

1094-6969

Type

jour

DOI

10.1109/5289.975463

Filename

975463