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Gwen Wu

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Proceedings Article | 3 February 2009 Paper

Demodulation of Fabry-Perot pressure sensors based on radial basis function network

Jing Wu, Ming Wang, Xiajuan Dai

Proceedings Volume 7157, 71570L (2009) https://doi.org/10.1117/12.808072

KEYWORDS: Sensors, Demodulation, Neurons, Wavelets, Fabry–Perot interferometers, Neural networks, Microelectromechanical systems, Fiber optics sensors, Optical sensors, Sensor technology

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In this paper, we present a demodulation of Fabry-Perot pressure sensor method based on radial basis function network(RBF). RBF network is a kind of three layers frontal feedback neural network with single connotative layer. It is proved that RBF is able to approach random continuous function with random precision. The cavity length variation is simulated from 473 to 483 µm with the step of 0.5 µm and the simulation result shows that the relative error of this new method is less than 0.02% and the maximum absolute error is less than 0.1 µm. The MEMS Fabry-Perot pressure sensor is also demodulated by the experiment. In the experiment, we change the pressure from 0 to 2 MPa with the step of 0.1 MPa. The experimental result shows that its linearity of the cavity length versus pressure achieves 0.98858 and the standard deviation between measured pressures and real pressures is less than 0.05 Mpa. By the experiment we can see that, this RBF network method can obtain upper precision and can reach the practice demand. This new method adapts to the practice demand with its higher resolution and less calculation time.

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