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首页> 外文期刊>The Journal of the Textile Institute >Modeling the sound absorption behavior of carpets using artificial intelligence
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Modeling the sound absorption behavior of carpets using artificial intelligence

机译：使用人工智能建模地毯的吸音行为

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The ability to provide some degree of noise attenuation is one of the most important properties of carpet. This is achieved by making the room in which the carpet is being installed less reverberant or minimizing the transmission of footstep noise through floors. This study aims to predict the sound absorption coefficient of acrylic carpet at different frequencies using three computational intelligence techniques viz., Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Interface System (ANFIS), and Genetic Algorithm (GA). To this end, carpets with different pile height and densities were produced. In order to simulate walking traffic, the carpets were exposed to 50, 100, 150, and 200 dynamic cycles. The sound absorption coefficient (SAC) of carpets was experimentally measured using a two-microphone impedance tube based on the transfer-function method. The effect of input parameters on SAC was statistically investigated, and the results showed that all parameters have a significant impact on SAC at a 95% confidence interval. To improve the prediction accuracy of the model, GA was implemented for the optimization of ANN and ANFIS parameters. The prediction accuracy of hybrid models ANN-GA and ANFIS-GA was compared with the traditional regression model by the mean absolute percentage error (MAPE). The results indicated that the prediction accuracy is considerably enhanced by using an optimized ANN and ANFIS structure. The MAPE for ANN-GA, ANFIS-GA, and regression models was found to be 11.85%, 17.68%, and 61.82%, respectively. The results demonstrated the applicability and performance of the hybrid ANN-GA model for the prediction of SAC of carpet.
机译：提供某种程度的噪音衰减的能力是地毯最重要的特性之一。这是通过制造地毯被安装的空间更加混响或最小化通过地板的脚步声噪声的传输来实现的。该研究旨在使用三种计算智能技术Viz预测不同频率下丙烯酸地毯的吸声系数。，人工神经网络（ANN），自适应神经模糊界面系统（ANFIS）和遗传算法（GA）。为此，生产具有不同桩高和密度的地毯。为了模拟行走流量，地毯暴露于50,100,150和200个动态周期。使用基于转移功能方法的双话筒阻抗管进行实验测量地毯的吸声系数（SAC）。输入参数对SAC的影响是统计学研究的，结果表明，所有参数对囊置信间隔95％的囊有显着影响。为了提高模型的预测准确性，已经实施了ANN和ANFI参数的优化。通过平均绝对百分比误差（MAPE）将混合模型和ANFIS-GA的预测精度与传统回归模型进行了比较。结果表明，使用优化的ANN和ANFIS结构，预测精度大大提高。发现Ann-Ga，ANFIS-GA和回归模型的MAPE分别为11.85％，17.68％和61.82％。结果证明了混合ANN-GA模型的适用性和性能，用于预测地毯囊的预测。

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