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Related Experiment Videos

Development of a virtual linearizer for correcting transducer static nonlinearity.

Amar Partap Singh1, Tara Singh Kamal, Shakti Kumar

  • 1Department of Electrical and Instrumentation Engineering, SLIET, Longowal, Punjab, India.

ISA Transactions
|July 22, 2006
PubMed
Summary

This study introduces an artificial neural network to correct transducer nonlinearity in computer-based measurement systems. This software-based approach offers an efficient solution for improving data accuracy in modern applications.

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Area of Science:

  • Measurement Science
  • Artificial Intelligence
  • Signal Processing

Background:

  • Transducer nonlinearity is a significant challenge in computer-based measurement systems.
  • Traditional analog linearization methods are often complex and costly.
  • The rise of microcomputers enables software-based solutions for linearization.

Purpose of the Study:

  • To develop an artificial neural network (ANN) based virtual linearizer.
  • To correct static nonlinearity in transducers within data-acquisition systems.
  • To provide an efficient software-based compensation method.

Main Methods:

  • Development of a multilayer feed-forward back-propagation neural network.
  • Training the network using the Levenberg-Marquardt learning rule.

Related Experiment Videos

  • Implementation as a virtual linearizer for transducer data.

    • Main Results:

    • The ANN effectively compensates for transducer static-nonlinearity.
    • The virtual linearizer provides an efficient software-based correction.
    • This method replaces traditional hardware-based analog linearization techniques.

    Conclusions:

    • Artificial neural networks offer an optimal solution for transducer linearization.
    • Software-based compensation is a viable and efficient alternative to analog methods.
    • The developed virtual linearizer enhances the accuracy of computer-based measurement systems.