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Evaluation and prediction of impact of noise on a worker in noisy environment by using ANFIS model

Author

Listed:
  • Tushar Kanta Mahapatra

    (KIIT Deemed to be University)

  • Suchismita Satapathy

    (KIIT Deemed to be University)

  • Subrat Kumar Panda

    (NIT Rourkela)

Abstract

A neuro-fuzzy computing system combines neural network learning with fuzzy model technique and interpretability into a single unit. Over the past ten years, several neuro-fuzzy systems have been developed. Among these, the adaptive neuro-fuzzy inference system (ANFIS) gives the best design parameters in the least period of time and offers a logical and focused technique for modelling. They are well known for accurately imitating a range of real-world obstacles. Noise pollution's impact on hearing is one such topic that is regularly raised. Specific occupational hazards for laborers include noise, vibration, and low temperatures. Investigating the combined effects of these three physical dangers on employees' physiological markers was the goal of this study, so these three main factors impacting hearing impact are temperature, vibration, and noise intensity, according to the research review. These characteristics interact in complicated, unreliable, and nonlinear ways. Because of this, it is difficult to completely explore using conventional methods. This paper develops a neuro-fuzzy model to predict how noise pollution would affect hearing as a function of noise intensity, vibration, and temperature. On the Fuzzy Logic Toolbox in MATLAB, the model is implemented using ANFIS. The specific fuzzy model that the authors created was employed to assist gather the data for the current investigation. The input/output data sets were split into two groups: 30% were used to assess the model's validity, and the remaining 70% were used to prepare the model for usage.

Suggested Citation

  • Tushar Kanta Mahapatra & Suchismita Satapathy & Subrat Kumar Panda, 2024. "Evaluation and prediction of impact of noise on a worker in noisy environment by using ANFIS model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(3), pages 1172-1182, March.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:3:d:10.1007_s13198-023-02198-3
    DOI: 10.1007/s13198-023-02198-3
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