Adaptive fuzzy neural network control on the acoustic field in a duct
Adaptive fuzzy neural network control on the acoustic field in a duct
复制标题
DOI：
10.1016/j.apacoust.2006.11.011
复制
发表时间：
2008-06-01
影响因子：
3.4
通讯作者：
Liu, Y. H.
中科院分区：
文献类型：
--
作者：
Chen, K. T.;Chou, C. H.;Liu, Y. H.
基于波的叠加原理，通过自适应调节次级源，使次级源产生与初级源同幅反相的反噪声，实现有源噪声控制。采用模糊神经网络与误差反向传播算法相结合的方法控制二次声源，对管道中的声衰减进行了研究。模糊推理系统最大的优点是将结构化知识以模糊IF-THEN规则的形式表示。但它缺乏适应外部环境变化的能力。将神经网络与模糊系统相结合，可以通过调整模糊集和创建模糊规则来帮助调整过程。在管道下游放置传声器，可以测量消声性能和控制误差。研究结果表明，该结构对纯音噪声的声衰减可达40 dB，对双音噪声的声衰减接近30 dB。(C)2006爱思唯尔有限公司保留所有权利。
Base on the principle of the superposition of waves, active noise control is achieved by adaptively tuning a secondary source which produces an anti-noise of equal amplitude and opposite phase with primary source. This paper presents the study on the acoustic attenuation in a duct by using the combination of fuzzy neural network with error back propagation algorithm to control secondary source. The most important advantage of fuzzy inference system is that the structured knowledge is represented in the form of fuzzy IF-THEN rules. But it lacks the ability to accommodate the change of external environments. Combining neural network with fuzzy system can help in this tuning process by adapting fuzzy sets and creating fuzzy rules. The performance of attenuation and control error can be measured by the microphone placed in the downstream of duct. The results of this study, show that the acoustic attenuation by 40 dB for pure-tone noise and nearly 30 dB for dual-tones noise are obtained. (C) 2006 Elsevier Ltd. All rights reserved.