Ahmed Hameed Kaleel, Zulquernain Mallick
Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi-110 025, India.
Noise Health. 2009 Oct-Dec;11(45):206-16. doi: 10.4103/1463-1741.56214.
Ration power plants, to generate power, have become common worldwide. One such one is the steam power plant. In such plants, various moving parts of heavy machines generate a lot of noise. Operators are subjected to high levels of noise. High noise level exposure leads to psychological as well physiological problems; different kinds of ill effects. It results in deteriorated work efficiency, although the exact nature of work performance is still unknown. To predict work efficiency deterioration, neuro-fuzzy tools are being used in research. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems. Among them, adaptive neuro-fuzzy inference system provides a systematic and directed approach for model building and gives the best possible design parameters in minimum possible time. This study aims to develop a neuro-fuzzy model to predict the effects of noise pollution on human work efficiency as a function of noise level, exposure time, and age of the operators doing complex type of task.
为了发电而建立的发电厂在全球已很常见。蒸汽发电厂就是其中之一。在这类工厂中，重型机器的各种运转部件会产生大量噪音。操作人员会遭受高强度噪音。长时间暴露在高噪音环境中会导致心理和生理问题，产生各种不良影响。尽管工作表现的确切性质尚不清楚，但这会导致工作效率下降。为了预测工作效率的下降，研究中正在使用神经模糊工具。已经证实，神经模糊计算系统有助于识别和分析模糊模型。在过去十年中，各种神经模糊系统有了显著发展。其中，自适应神经模糊推理系统为模型构建提供了一种系统且有针对性的方法，并能在尽可能短的时间内给出最佳设计参数。本研究旨在开发一种神经模糊模型，以预测噪音污染对从事复杂任务的操作人员的工作效率的影响，该影响是噪音水平、暴露时间和操作人员年龄的函数。
