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

Nilanjan Byabarta et al.

Journal of Statistics and Management Systems2026https://doi.org/10.47974/jsms-1526article
ABDC C
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.47974/jsms-1526

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@article{nilanjan2026,
  title        = {{Adaptive sensor linearization : A hybrid approach utilizing polynomial, spline, RBF and deep neural network methods}},
  author       = {Nilanjan Byabarta et al.},
  journal      = {Journal of Statistics and Management Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.47974/jsms-1526},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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