Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Sensor calibration and compensation using artificial neural network.

Shakeb A Khan1, D T Shahani, A K Agarwala

  • 1Indian Institute of Technology, New Delhi 110016, India.

ISA Transactions
|July 16, 2003
PubMed
Summary

This article explores how artificial neural networks can improve the accuracy of sensors by correcting non-linear responses. The authors provide guidelines for choosing model settings to achieve desired error levels efficiently. They also compare this approach to older methods, highlighting benefits for hardware implementation.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Influence of nanofluid on performance improvement of dielectric properties and dissolved gases in oil-immersed power transformers.

Discover nano·2026
Same author

A case of bite by Krait snake.

The Journal of the Association of Physicians of India·1979
See all related articles

Area of Science:

  • Instrumentation engineering within artificial neural network research
  • Signal processing and sensor systems engineering

Background:

Engineers often struggle to linearize sensor outputs due to inherent non-linear characteristics that limit measurement precision. Prior research has shown that traditional regression methods frequently fail to handle complex, non-linear sensor behaviors effectively. That uncertainty drove the development of advanced computational approaches to improve signal accuracy. No prior work had fully resolved the relationship between model complexity and calibration efficiency for these systems. This paper addresses the gap by examining how specific design parameters influence overall performance. The authors investigate how model order and calibration point density impact the final error rates. Understanding these factors is necessary for optimizing sensor performance in practical applications. This study provides a framework for selecting parameters to meet strict error requirements in instrumentation design.

Purpose Of The Study:

The aim of this study is to investigate the use of inverse modeling techniques for sensor response linearization. Researchers seek to address the challenges associated with non-linear characteristics in sensor systems. This work focuses on identifying the influence of model order on overall measurement accuracy. The authors also examine how the number of calibration points affects the final error rates. They intend to provide clear guidelines for engineers to fix these design parameters efficiently. The motivation stems from the need for faster and more reliable sensor compensation methods. This study also explores the benefits of neural networks over traditional regression models for hardware implementation. The authors aim to provide results that assist instrumentation design engineers in optimizing their systems.

Keywords:
instrumentation designsignal linearizationinverse modelingmicrocontroller implementation

Frequently Asked Questions

The researchers propose an inverse modeling technique utilizing artificial neural networks to linearize sensor responses. This method reduces the asymptotic root-mean-square error by optimizing the model order and the density of calibration points, which outperforms traditional regression-based approaches in hardware implementation simplicity.

The authors emphasize the model order and the number of calibration points as the two primary design parameters. These factors determine the convergence time and the final error rate, whereas traditional regression methods lack this specific flexibility in microcontroller-based hardware configurations.

The authors suggest that these parameters are necessary to achieve a target root-mean-square error starting from a known initial nonlinearity. This approach allows engineers to fix model settings rapidly, unlike older regression techniques that may require more complex adjustments for similar performance levels.

Related Experiment Videos

Main Methods:

The authors employ an inverse modeling approach to analyze the linearization of sensor responses. Their review approach involves evaluating the impact of model order on system performance. They systematically vary the number of calibration points to observe changes in error metrics. The team utilizes iterative training cycles to determine the convergence time for the models. They compare these findings against traditional regression-based techniques to highlight performance differences. The study focuses on the practical requirements for microcontroller-based hardware implementations. Researchers document the number of epochs needed for both initial setup and drift compensation. This methodology provides a comprehensive assessment of how design choices affect the final accuracy of the sensors.

Main Results:

The study reveals that the inverse modeling technique successfully achieves linearization of sensor responses. The authors report that the choice of model order and calibration points directly dictates the lowest asymptotic root-mean-square error. Their findings show that specific parameter combinations allow for rapid determination of system requirements based on initial nonlinearity. The results demonstrate that the number of training epochs serves as a reliable indicator of convergence time. The researchers observe that this method offers superior hardware simplicity compared to standard regression approaches. They provide data on the epochs needed to recalibrate sensors during sensitivity or offset drifts. The evidence suggests that these design guidelines are highly relevant for instrumentation engineers. These findings establish a clear relationship between computational design parameters and the resulting sensor performance accuracy.

Conclusions:

The authors demonstrate that their inverse modeling approach effectively linearizes sensor responses compared to standard regression techniques. Their synthesis suggests that selecting optimal model orders significantly reduces asymptotic root-mean-square error values. The study implies that hardware simplicity is a major benefit when deploying these models on microcontrollers. Researchers propose that the provided results allow engineers to quickly determine necessary calibration points for specific nonlinearity levels. The findings indicate that convergence times for initial calibration and subsequent drift compensation are predictable. The authors suggest that this methodology offers a robust alternative for instrumentation design tasks. Their work highlights the practical utility of neural networks in overcoming traditional modeling limitations. These implications provide a clear path for implementing efficient sensor compensation strategies in real-world systems.

The researchers utilize the number of epochs to measure the convergence time required for both initial calibration and subsequent recalibration. This data type helps quantify the efficiency of the neural network, contrasting with the static nature of traditional regression models that do not account for iterative training cycles.

The study measures the lowest asymptotic root-mean-square error to evaluate performance. This metric provides a quantitative assessment of linearization success, whereas traditional regression methods are often evaluated based on simpler curve-fitting metrics that may not capture the same level of non-linear complexity.

The authors claim that their approach provides significant advantages for hardware simplicity in microcontroller-based implementations. They propose that this method is more efficient than traditional regression, offering a practical solution for instrumentation engineers who need to manage sensitivity or offset drifts in sensor systems.