The temperature compensation application of the improved fuzzy neural network in the oil viscous force system

Author

Zhou, Shiru ; Tian, Jingwen ; Gao, Meijuan

Author_Institution

Beijing Union Univ., Beijing, China

fYear

2009

fDate

16-19 Aug. 2009

Abstract

The strain gauge sensor in the oil viscous force measurement system affected by the environmental factors generated temperature drift, resulting in decreased accuracy of measurement, this paper presents an temperature compensation method based on the improved fuzzy neural network, the use of fuzzy neural network nonlinear mapping ability to build the network, using a new genetic and the ant colony hybrid algorithm to optimize the network, making the accuracy of the network can be improved so that strain gauge can be achieved smart temperature error compensation.

Keywords

compensation; force measurement; fuzzy neural nets; genetic algorithms; oils; strain gauges; strain sensors; viscosity; ant colony hybrid algorithm; fuzzy neural network; genetic algorithm; nonlinear mapping ability; oil viscous force measurement system; strain gauge sensor; temperature compensation application; Capacitive sensors; Environmental factors; Force measurement; Force sensors; Fuzzy neural networks; Hybrid power systems; Petroleum; Sensor systems; Strain measurement; Temperature sensors; ant colony algorithm; fuzzy neural network; genetic algorithm; temperature compensation;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronic Measurement & Instruments, 2009. ICEMI '09. 9th International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-3863-1

Electronic_ISBN

978-1-4244-3864-8

Type

conf

DOI

10.1109/ICEMI.2009.5274294

Filename

5274294