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Interval type-2 fuzzy neural network controller for a multivariable anesthesia system based on a hardware-in-the-loop

Ahmad M El-Nagar1, Mohammad El-Bardini1

  • 1Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menofia University, Menouf 32852, Egypt.

Artificial Intelligence in Medicine
|April 8, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an interval type-2 fuzzy neural network (IT2FNN) controller for anesthesia systems. The IT2FNN controller demonstrates superior performance in managing patient variability and surgical stimulation compared to other methods.

Keywords:
AnalgesiaAnesthesiaBack-propagation algorithmHardware-in-the-loopInterval type-2 fuzzy neural networkMuscle relaxation

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Area of Science:

  • Biomedical Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Anesthesia systems require precise control to manage patient variability and surgical stimuli.
  • Traditional controllers face challenges in handling the inherent uncertainties in physiological parameters.
  • Interval type-2 fuzzy logic systems offer enhanced capabilities for managing uncertainty.

Purpose of the Study:

  • To develop and evaluate an interval type-2 fuzzy neural network (IT2FNN) controller for a multivariable anesthesia system.
  • To assess the IT2FNN controller's ability to minimize the effects of surgical stimulation.
  • To overcome the uncertainty problem caused by inter-individual variability in patient parameters.

Main Methods:

  • A hardware-in-the-loop simulation was employed to test the IT2FNN controller.
  • The IT2FNN controller integrates an interval type-2 fuzzy linguistic process and an interval neural network.
  • Controller parameters were trained online using a back-propagation algorithm.

Main Results:

  • The IT2FNN controller demonstrated robust performance across a wide range of patient parameters.
  • Experimental results showed superior performance compared to type-1 fuzzy neural network (T1FNN) and adaptive interval type-2 fuzzy logic controllers (AIT2FLC).
  • The IT2FNN controller exhibited reduced settling time and overshoot, with lower performance indices.

Conclusions:

  • The IT2FNN controller is more effective than T1FNN controllers in handling uncertain information.
  • The inherent structure of type-2 fuzzy logic systems (FLSs) allows for modeling and minimizing numerical and linguistic uncertainties.
  • IT2FNN controllers offer a promising approach for advanced anesthesia system control.