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Intelligent sensors using computationally efficient Chebyshev neural networks

Intelligent sensors using computationally efficient Chebyshev neural networks

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Intelligent signal processing techniques are required for auto-calibration of sensors, and to take care of nonlinearity compensation and mitigation of the undesirable effects of environmental parameters on sensor output. This is required for accurate and reliable readout of the measurand, especially when the sensor is operating in harsh operating conditions. A novel computationally efficient Chebyshev neural network (CNN) model that effectively compensates for such non-idealities, linearises and calibrates automatically is proposed. By taking an example of a capacitive pressure sensor, through extensive simulation studies it is shown that performance of the CNN-based sensor model is similar to that of a multilayer perceptron-based model, but the former has much lower computational requirement. The CNN model is capable of producing pressure readout with a full-scale error of only ±1.0% over a wide operating range of −50 to 200°C.

Inspec keywords: neural nets; intelligent sensors; signal processing; pressure sensors; capacitive sensors; Chebyshev approximation

Other keywords: Chebyshev neural networks; temperature -50 degC to 200 degC; nonlinearity compensation; intelligent sensors; intelligent signal processing techniques; environmental parameters; capacitive pressure sensor

Subjects: Intelligent sensors; Digital signal processing; Interpolation and function approximation (numerical analysis); Signal processing and detection; Interpolation and function approximation (numerical analysis); Neural computing techniques; Pressure and vacuum measurement

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