ANN-based signal conditioning and its hardware implementation of a nanostructured porous silicon relative humidity sensor

Porous silicon (PS) is a potential candidate for developing a low cost smart relative humidity sensor, but its main limitation is that the response is a nonlinear function of humidity, suffering from hysteresis and drift due to aging. The present work proposes novel ANN-based signal conditioning to compensate the nonlinearity and hysteresis errors of a PS humidity sensor by using a soft computing technique based on an adaptive linear (ADALINE) neural network. The proposed network is simple to implement and requires less hardware elements. The ANN model is implemented in hardware using a microcontroller (AT89C51) to make the sensor output be direct digital readout. Minimum calibrating data points necessary for developing the model for compensating the errors are also established. Experimental and simulation studies show that nonlinearity is reduced to 2.7% from its initial 13% while the hysteresis error is reduced to 3% from its initial 16% value for a typical PS humidity sensor. The outputs of a hardware-implemented circuit follow the results of simulation. The proposed signal conditioning can be extended for other sensors with and without hysteresis.