Neural networks applied to continuous range amplitude measurements of small sinusoidal vibrations [using Michelson interferometer]

Author

Cimponeriu, Andrei

Author_Institution

Dept. of Meas. & Opt. Electron., Polytech.. Univ. of Timisoara, Romania

Volume

3

fYear

1999

fDate

1999

Firstpage

1337

Abstract

A novel method for computing the amplitude of small sinusoidal vibrations measured with the Michelson interferometer is proposed. The method is a particular case of nonlinear adaptive inverse modeling, using neural networks. The feasibility of the method is proved by simulation, and the noise behavior is examined. The results are of practical interest: they show that the method is applicable to practical, noisy signals and the precision can be very good

Keywords

Bessel functions; Michelson interferometers; adaptive filters; backpropagation; feedforward neural nets; filtering theory; inverse problems; light interferometry; mean square error methods; measurement by laser beam; modelling; nonlinear filters; signal sampling; vibration measurement; Bessel functions; Michelson interferometer; backpropagation; continuous range amplitude measurements; filtering; high precision; mean square error; neural networks; noise behavior; nonlinear adaptive inverse modeling; simulation; small sinusoidal vibrations; Computational modeling; Computer networks; Equations; Frequency measurement; Inverse problems; Lighting; Mirrors; Neural networks; Particle measurements; Vibration measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 1999. IMTC/99. Proceedings of the 16th IEEE

Conference_Location

Venice

ISSN

1091-5281

Print_ISBN

0-7803-5276-9

Type

conf

DOI

10.1109/IMTC.1999.776023

Filename

776023