Paper
31 December 2008 Nonlinear identification of eddy current sensors based on artificial neural networks
Hairong Zou
Author Affiliations +
Proceedings Volume 7130, Fourth International Symposium on Precision Mechanical Measurements; 713013 (2008) https://doi.org/10.1117/12.819577
Event: Fourth International Symposium on Precision Mechanical Measurements, 2008, Anhui, China
Abstract
The nonlinear identification of the input-output characteristic of the eddy current sensor is an important research task in the application of the eddy current sensor. At first, the working principle and the working characteristics of the eddy current sensor were introduced in this paper. Then the nonlinear identification of the eddy current sensor was analyzed in detail. To aim at the error problem of the eddy current sensor with the nonlinear factors, a feasible project of nonlinear identification of the eddy current sensor was researched applied the modeling method of artificial neural networks, and a new artificial neural network is presented to realize the nonlinear identification of the eddy current sensor without linearization processing. Its design, modeling and implement technology based on a new internal recurrent neural networks were advanced, and then the data experiments were processed with MATLAB software, thus the measure precision of the eddy current sensor was enhanced. In the end, the measuring error is no more than 0.1%, and the dynamic responding time is less than 0.5 second. This method can increase the measuring accuracy and be advantageous for the test analysis and the data statistics though the online data processing of the computer.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hairong Zou "Nonlinear identification of eddy current sensors based on artificial neural networks", Proc. SPIE 7130, Fourth International Symposium on Precision Mechanical Measurements, 713013 (31 December 2008); https://doi.org/10.1117/12.819577
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KEYWORDS
Sensors

Artificial neural networks

Neurons

Data processing

Neural networks

Magnetism

Analytical research

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