Neural-Network-Based Robust Linearization and Compensation Technique for Sensors Under Nonlinear Environmental Influences

Abstract

A novel artificial neural network (NN)-based technique is proposed for enabling smart sensors to operate in harsh environments. The NN-based sensor model automatically linearizes and compensates for the adverse effects arising due to nonlinear response characteristics and nonlinear dependency of the sensor characteristics on the environmental variables. To show the potential of the proposed NN-based technique, we have provided results of a smart capacitive pressure sensor (CPS) operating under a wide range of temperature variation. A multilayer perceptron is utilized to transfer the nonlinear CPS characteristics at any operating temperature to a linearized response characteristics. Through extensive simulated experiments, we have shown that the NN-based CPS model can provide pressure readout with a maximum full-scale error of only 1.5% over a temperature range of 50 to 200 with excellent linearized response for all the three forms of nonlinear dependencies considered. Performance of the proposed technique is compared with a recently proposed computationally efficient NN-based extreme learning machine. The proposed multilayer perceptron based model is tested by using experimentally measured real sensor data, and found to have satisfactory performance.