Shock and Vibration

Shock and Vibration / 2001 / Article

Open Access

Volume 8 |Article ID 604583 | https://doi.org/10.1155/2001/604583

Zhang Qizhi, Jia Yongle, "Active Noise Feedback Control Using a Neural Network", Shock and Vibration, vol. 8, Article ID 604583, 5 pages, 2001. https://doi.org/10.1155/2001/604583

Active Noise Feedback Control Using a Neural Network

Received02 May 2000
Revised07 Sep 2000

Abstract

The active noise control (ANC) is discussed. Many digital ANC systems often based on the filter-x algorithm for finite impulse response (FIR) filter use adaptive filtering techniques. But if the primary noise path is nonlinear, the control system based on adaptive filter technology will be invalid. In this paper, an adaptive active nonlinear noise feedback control approach using a neural network is derived. The feedback control system drives a secondary signal to destructively interfere with the original noise to cut down the noise power. An on-line learning algorithm based on the error gradient descent method was proposed, and the local stability of closed loop system is proved using the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise feedback control method based on a neural network is very effective to the nonlinear noise control.

Copyright © 2001 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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