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 co...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/94350 http://hdl.handle.net/10220/7092 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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°C. A microcontroller unit-based implementation scheme is also proposed. |
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