Neural-network-based smart sensor framework operating in a harsh environment
We present an artificial neural-network- (NN-) based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/94140 http://hdl.handle.net/10220/7115 |
| Summary: | We present an artificial neural-network- (NN-) based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we provide results of a smart capacitive pressure sensor (CPS) operating in a wide temperature range of 0 to 250° C. Through simulated experiments, we have shown that the NN-based CPS model is capable of providing pressure readout with a maximum full-scale (FS) error of only ±1.0% over this temperature range. A novel scheme for estimating the ambient temperature from the sensor characteristics itself is proposed. For this purpose, a second NN is utilized to estimate the ambient temperature accurately from the knowledge of the offset capacitance of the CPS. A microcontroller-unit- (MCU-) based implementation scheme is also provided. |
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