A multilayer artificial neural network (ANN) is proposed for modeling of a
capacitive pressure sensor (CPS). When the ambient temperature changes over
a wide range, the nonlinear response characteristics of a CPS change signi
ficantly. In many practical conditions, the effect of temperature on the ch
ange in the CPS characteristics may be nonlinear. The proposed ANN model ca
n provide correct readout of the applied pressure under such conditions. A
novel scheme for estimation of the ambient temperature from the sensor char
acteristics itself is proposed. A second ANN is utilized to estimate the am
bient temperature from the knowledge of the offset capacitance, i.e., the z
ero-pressure capacitance. A microcontroller unit (MCU)-based implementation
scheme for this model is also considered. Simulation results show that thi
s model can estimate the pressure with a maximum error of +/- 2% over a wid
e variation of temperature from - 50 degreesC to 150 degreesC. (C) 2000 Els
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