Auto-compensation of nonlinear influence of environmental parameters on the sensor characteristics using neural networks.
Usually the environmental parameters influence the sensor characteristics in a nonlinear manner. Therefore obtaining correct readout from a sensor under varying environmental conditions is a complex problem. In this paper we propose a neural network (NN)-based interface framework to automatically compensate for the nonlinear influence of the environmental temperature and the nonlinear-response characteristics of a capacitive pressure sensor (CPS) to provide correct readout. With extensive simulation studies we have shown that the NN-based inverse model of the CPS can estimate the applied pressure with a maximum error of +/- 1.0% for a wide temperature variation from 0 to 250 degrees C. A microcontroller unit-based implementation scheme is also proposed.
Patra
Jagdish Chandra
JC
School of Computer Engineering, Nanyang Technological University, Singapore 639798. aspatra@ntu.edu.sg
Ang
Ee Luang
EL
Das
Amitabha
A
Chaudhari
Narendra Shivaji
NS
D000465:Algorithms; D002138:Calibration; D003198:Computer Simulation; D004582:Electronics, Medical; D004777:Environment; D004784:Environmental Monitoring / Q000295:instrumentation* / Q000379:methods; D004867:Equipment Design / Q000379:methods*; D019544:Equipment Failure Analysis / Q000379:methods*; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D014159:Transducers