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Related Experiment Video

Updated: Jun 20, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Adaptive inferential sensors based on evolving fuzzy models.

Plamen Angelov1, Arthur Kordon

  • 1Intelligent Systems Research Laboratory, Infolab21, Lancaster University, Lancaster, UK. p.angelov@lancaster.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 25, 2009
PubMed
Summary
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This study introduces evolving fuzzy models (EFMs) for creating adaptive, self-calibrating inferential sensors. These novel eSensors reduce maintenance costs in the process industry while maintaining high precision and interpretability.

Area of Science:

  • Process Engineering
  • Artificial Intelligence
  • Chemical Industry

Background:

  • Modern process industries require adaptive, self-calibrating online inferential sensors.
  • Existing sensors often incur high maintenance costs and can lack transparency.
  • There is a need for intelligent systems that can automatically adapt to changing process conditions.

Purpose of the Study:

  • To propose a new technique for designing and utilizing inferential sensors based on evolving fuzzy models (EFMs).
  • To develop adaptive and self-calibrating online inferential sensors that minimize maintenance costs and maximize precision.
  • To enable automatic recalibration of inferential sensors, reducing life-cycle maintenance efforts.

Main Methods:

  • Utilized the concept of evolving fuzzy models (EFMs) with adaptive and flexible open structures.

Related Experiment Videos

Last Updated: Jun 20, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Developed a methodology for online automatic selection of relevant input variables for prediction.
  • Implemented techniques for automatic detection of data pattern shifts using cluster age and fuzzy rules.
  • Employed an online standardization technique within the evolving model's learning procedure.
  • Applied the approach to real-life industrial processes in the chemical industry, specifically at The Dow Chemical Company.

    • Main Results:

    • Demonstrated the automatic design of interpretable, simple-structure inferential sensors from real-time data streams.
    • Successfully predicted various process variables of interest using the developed eSensors.
    • Showcased the effectiveness of automatic recalibration, reducing maintenance needs.
    • Validated the approach on industrial chemical processes, predicting product properties.

    Conclusions:

    • The proposed methodology enables the creation of a new generation of adaptive and evolving inferential sensors.
    • These eSensors address key challenges in the modern advanced process industry.
    • The approach offers a foundation for developing intelligent, self-maintaining sensor systems.
    • The findings are applicable to the broader chemical and process industries.