Intelligent calibration of level sensors using functional link neural networks with piecewise linear interpolation

Abstract

This Study focuses on designing an intelligent calibration model for level sensors by using the Functional Link Artificial Neural Network (FLANN). The FLANN has simple architecture and requires less computational effort compared to other neural networks models. This made it good enough in extending the linearity of many sensors and transducers as reported in recent literature. Despite its advantages, the standard FLANN model has limitations in terms of generalization and accuracy. To overcome this shortcoming, we propose in this study an approach that relies on integrating Piecewise Linear (PWL) interpolation with the FLANN model. This approach aims to improve the overall performance of the calibration process, offering better generalization capabilities. Building on an extensive experimental investigation of the intelligent calibration model on the typical problem of level sensors calibration, this manuscript outlines the methodology, implementation steps, and findings of our study.