SBIR Phase I: A Neural Network Approach for Active Noise Control in a Three-Dimensional Enclosure

SBIR 第一阶段：三维外壳中主动噪声控制的神经网络方法

• 批准号: 9660605
• 负责人: Donald Sofge
• 金额: $ 7.5万
• 依托单位: NeuroDyne Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-01-01 至 1997-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9660605&HistoricalAwards=false
• 关键词: SBIR; Phase; Neural; Network; Approach

*** ABSTRACT 9660605 Sofge This Small Business Innovation Research Phase I project will develop and test a new approach to active interior noise control in a three-dimensional enclosure. The new thrust in the proposed research is a digital adaptive system that makes use of both analyses and directly measured data from a three-dimensional enclosure to card out active noise control by using smart materials located on flexible boundaries of the enclosure. The system is intended to track the changes in the acoustic information due to movement, changes in open apertures, etc. and carry out on-line minimization to realize the desired acoustic performance. Unlike most methods that assume that the acoustic field inside the enclosure can be modeled by using standing waves, the proposed method is designed to accommodate diffuse sound fields in the enclosure. Furthermore, the noise is not assumed to be harmonic and nonperiodic sound fields are to be considered. The neural network based filter proposed herein can make use of analytical and experimental data to assign initial weights that are to be adapted to carry out (locally) on-line minimization of sound pressure in the enclosure. Noise reduction in aircraft is a subject of serious commercial concern in the industry. The techniques developed in this effort will be widely applicable to noise reduction in rotorcraft cabins of commercial helicopters, and may be adapted for use in commercial airline cockpits, as well as in factory settings and other vibration critical manufacturing environments. ***

* 摘要 小行星9660605 这个小型企业创新研究第一阶段项目将开发和测试一种新的方法，在三维外壳中进行主动内部噪声控制。拟议研究的新重点是一个数字自适应系统，该系统利用来自三维外壳的分析和直接测量数据，通过使用位于外壳柔性边界上的智能材料来卡出主动噪声控制。该系统旨在跟踪由于移动、开口孔径变化等引起的声学信息的变化，并进行在线最小化以实现期望的声学性能。与大多数方法不同的是，假设可以通过使用驻波来模拟外壳内部的声场，所提出的方法被设计为适应外壳中的扩散声场。此外，噪声不被假定为谐波和非周期性的声场被认为是。本文提出的基于神经网络的滤波器可以利用分析和实验数据来分配初始权重，所述初始权重将适于执行（局部）封闭空间中声压的在线最小化。 降低飞机的噪音是工业中严重的商业关注的主题。 在这项工作中开发的技术将广泛适用于商用直升机旋翼机机舱的降噪，并可适用于商用航空公司驾驶舱，以及工厂设置和其他振动关键制造环境。***