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 C 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.

Implementation of software-based sensor linearization algorithms on low-cost microcontrollers

“…Hardware implementation of an ANN that requires parallel processing is a design difficulty. Although the hardware implementation of ANNs has been recommended by using processors like FPGA [19], there are some microcontroller-based ANN applications. For programming of an ANN in a microcontroller, an offline training step must be achieved.…”

Hermite neural network-based intelligent sensors for harsh environments

“…An MLP-based NN to linearize the sensor characteristics and compensate for the nonlinear influence of environmental parameters on sensor characteristics with quite satisfactory results have been reported [16]. NN-based dynamic compensation of sensors for wireless sensor applications with excellent results have been reports [17], [18].…”

Neural Network Based Real-time Correction of Transducer Dynamic Errors

“…Classical dynamic error correction algorithms are usually characterized by high complexity of numerical operations, in particular in the case of describing the transducer dynamics by means of higher order differential equations. ANN as "universal approximators" [12,13,14] have been widely used for transducer static error correction [15,16,17,18], in particular for transducer and measuring instrument calibration [19,20,21]. Nevertheless, in the field of realtime dynamic error correction, solutions using DSP [22,23,24], FPGA technique [25] and analog circuits [26,27] are dominant.…”