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Optimization of the ANFIS using a genetic algorithm for physical work rate classification.

Ehsanollah Habibi1, Mina Salehi1, Ghasem Yadegarfar2

  • 1Department of Occupational Health Engineering, Isfahan University of Medical Sciences, Iran.

International Journal of Occupational Safety and Ergonomics : JOSE
|February 10, 2018
PubMed
Summary

Optimizing the adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) significantly improves physical work rate classification accuracy. This enhanced model offers a more precise and reliable method for assessing physical exertion levels.

Keywords:
adaptive neuro-fuzzy inference systemclassificationoptimizationphysical work rate

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Area of Science:

  • Physiology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Accurate physical work rate classification is crucial for various applications, including occupational health and sports science.
  • Existing methods, such as adaptive neuro-fuzzy inference systems (ANFIS), show promise but can be further optimized for enhanced performance.

Purpose of the Study:

  • To develop and evaluate a genetic algorithm (GA)-optimized ANFIS model for highly accurate physical work rate classification.
  • To demonstrate the superiority of the GA-optimized ANFIS model compared to the standard ANFIS model.

Main Methods:

  • Thirty healthy male participants were recruited for laboratory-based measurements.
  • Heart rate and oxygen consumption data were collected and used to train the ANFIS classifier in MATLAB.
  • A genetic algorithm (GA) was employed to optimize the ANFIS model parameters.

Main Results:

  • The GA-optimized ANFIS model achieved a mean accuracy of 97.92%, an increase from the standard ANFIS model's 92.95%.
  • Root mean square error was reduced from 5.4186 to 3.1882, indicating improved model precision.
  • The optimized model demonstrated a maximum estimation error of ±5% during network testing.

Conclusions:

  • Genetic algorithm (GA) optimization effectively enhances the performance of ANFIS for physical work rate classification.
  • The GA-optimized ANFIS model provides a highly accurate, simple to implement, and adaptable solution for assessing physical work rate, considering inter-individual variability.