An approximation network for measurement systems

Author

Zaremba, M.B. ; Porada, E. ; Bock, W.J.

Author_Institution

Dept. d´´Inf., Quebec Univ., Hull, Que., Canada

fYear

1993

fDate

1993

Firstpage

1713

Abstract

The problem of extraction of the measured value in optical measurement systems is addressed. It is required that the values of the calibration points be recreated exactly, while maintaining precise approximation between the points. A neural processing method is provided to solve the problem. A two-layer feed-forward network that offers a possibility of on-going insight into the approximation precision, the optimal selection of the successive training layouts, and the linear separability of the training inputs is constructed. Examples are given to illustrate the proposed method

Keywords

calibration; feedforward neural nets; learning (artificial intelligence); optical variables measurement; approximation network; approximation precision; calibration points; linear separability; measured value; neural processing method; optical measurement systems; training layouts; two-layer feed-forward network; Calibration; Multi-layer neural network; Neural networks; Neurons; Optical sensors; Optical signal processing; Sensor systems; Signal processing; Strain measurement; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1993., IEEE International Conference on

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-0999-5

Type

conf

DOI

10.1109/ICNN.1993.298815

Filename

298815