Title :
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
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;
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
DOI :
10.1109/ICEMI.2009.5274294
