Artificial Neural Networks and Advanced Fuzzy Techniques for Predicting Noise Level in the Industrial Embroidery Workrooms

Abstract

Noise prediction techniques are considered to be an important tool for evaluating cost-effective noise control measures in industrial workrooms. One of the most important issues in this regard is the development of accurate methods for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, artificial neural networks and advanced fuzzy techniques were employed to develop a relatively accurate model for noise prediction in the noisy process of industrial embroidery. The data were collected from 60 embroidery workrooms. Some acoustical descriptors of workrooms were selected as input features based on International Organization for Standardization (ISO) 11690-3. Prediction errors of all structures associated with neural networks and fuzzy models were approximately similar and lower than 1 dB. However, neurofuzzy models could slightly improve the accuracy of noise prediction compared with neural networks. These results confirmed that these techniques can be regarded as useful tools for occupational health professionals in order to design, implement, and evaluate various noise control measures in noisy workrooms.