A fuzzy logic-based model for noise control at industrial workplaces

Aluclu, I.; Dalgic, A; Toprak, Z. Fuat

doi:10.1016/j.apergo.2007.08.005

Cited by 47 publications

(29 citation statements)

References 7 publications

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“…Therefore, the stream flow was classified according to the months. A similar consideration can be seen in studies on fuzzy classification and fuzzy control systems [24]. The MFs of CS and CT are trapezoidal, whereas the remaining are triangular, as presented in Fig.…”

Section: Development Of the Fuzzy Modelmentioning

confidence: 74%

Modeling Monthly Mean Flow in a Poorly Gauged Basin by Fuzzy Logic

Toprak¹,

Eriş²,

Ağıralioğlu³

et al. 2009

CLEAN Soil Air Water

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The estimation of the monthly mean flow is a critical issue in many water resource development projects. However, in practice the mean flow is not easily determined in ungauged and poorly gauged basins. Therefore, in the literature, various flow estimation methods have been developed recently for mountainous regions which are generally ungauged or poorly gauged basins. In this study a fuzzy logic model based on the Mamdani approach was developed to estimate the flow for poorly gauged mountainous basins. This model was applied to the Solakli Basin which is located in the Eastern Black Sea Region of Turkey. Limited rainfall and flow data are available for this basin. In addition to these variables, the stream and time coefficients were introduced and used as variables for modeling. The data was divided into training and testing phases. The model results were compared with the measured data. The comparison depends on seven statistical characteristics, four different error modes and the contour map method. It was observed that the fuzzy model developed in this study yielded reliable results.

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Section: Development Of the Fuzzy Modelmentioning

confidence: 74%

Modeling Monthly Mean Flow in a Poorly Gauged Basin by Fuzzy Logic

Toprak¹,

Eriş²,

Ağıralioğlu³

et al. 2009

CLEAN Soil Air Water

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“…It was specifically developed to mathematically present uncertainty and vagueness and makes formalized implements for dealing with the imprecision intrinsic to many real situations (Fisher 2003). The results of applying fuzzy logic to the development of noise control models and acoustic comfort optimization in industrial workplaces suggest that fuzzy logic can be utilized to analyze very complex processes and can be successfully applied to highly nonlinear systems (Aluclu, Dalgic, and Toprak 2008;Zaheerudin and Jain 2008).…”

Section: Introductionmentioning

confidence: 99%

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

Aliabadi

Golmohammadi

Khotanlou

et al. 2015

Applied Artificial Intelligence

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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.

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“…It has also been verified that simple tasks are not affected even at very high noise level while complex tasks get significantly affected at much lower noise level. Aluclu, et al (2008) [23] They described noise-human response and a fuzzy logic model developed by comprehensive field studies on noise measurements (including atmospheric parameters) and control measures. The model has two subsystems constructed on noise reduction quantity in dB.…”

Section: Studies On Noise Effects Related To Task Performancementioning

confidence: 99%