Abstract A novel artificial neural network (ANN)-based intelligent capacitive pressure sensor (CPS) in noisy environment is proposed in this paper. A switched capacitor circuit (SCC) is used to convert the change in capacitance of the CPS due to applied pressure into a proportional voltage which is then applied to the ANN model to estimate the pressure. Because of the nonlinear response characteristics of the CPS and its temperature dependence, complex signal processing of the SCC output is required to estimate the applied pressure accurately, especially when the room temperature changes with time, place, or both. The situation becomes further complicated when the CPS encounters random noise, as is the case in many practical situations. To alleviate these difficulties in estimation of unknown applied pressure in a CPS, a multilayer perceptron (MLP) has been utilized to model the CPS characteristics over a wide temperature range with noise. By training the MLP model suitably, a direct digital readout of the applied pressure can be obtained. From the simulation studies it was verified that the performance of this model is quite satisfactory for a wide variation of temperature, starting from −20°C to 70°C, and for a signal-to-noise ratio (SNR) of 40 dB and above. This modeling technique provides greater flexibility and accuracy in a changing and noisy environment.
[1]
P. T. Kolen,et al.
Self-calibration/compensation technique for microcontroller-based sensor arrays
,
1994
.
[2]
Kenzo Watanabe,et al.
A switched-capacitor interface for capacitive pressure sensors
,
1991,
[1991] Conference Record. IEEE Instrumentation and Measurement Technology Conference.
[3]
Jagdish C. Patra.
An artificial neural network-based smart capacitive pressure sensor
,
1997
.
[4]
M. Yamada,et al.
A capacitive pressure sensor interface using oversampling /spl Delta/-/spl Sigma/ demodulation techniques
,
1997
.
[5]
Ganapati Panda,et al.
Artificial neural network-based nonlinearity estimation of pressure sensors
,
1994
.