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Open Access Research Article

Adaptive sensor linearization : A hybrid approach utilizing polynomial, spline, RBF and deep neural network methods

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* Corresponding author · click or hover a name for details

pp. 101–110Vol. 29Issue 1January 2026DOI: 10.47974/JSMS-1526XML
Received:
11 Mar 2025
Published Online:
24 Nov 2025
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1526
Pages:
101–110

Abstract

Radial basis function (RBF) networks, deep neural networks (DNN) with Adam optimization, spline interpolation, polynomial approximation, and DNN with Levenberg-Marquardt (LM) optimization are five sophisticated techniques used in this work to build a novel universal linearization framework. Through adaptive mode selection of the most appropriate technique depending on the unique characteristics of the sensor data, the proposed system achieves higher accuracy and robustness in handling diverse nonlinearities. Experimental results demonstrate remarkable improvement in linearization performance for various kinds of thermocouple sensors, witnessing the usability and efficiency of the framework for real-time applications.

Keywords

Subject Classifications

13P25

References

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