Casper K. ChenTZI-DAR CHIUEHJYH-HORNG CHEN2018-09-102018-09-101998-0809168532http://scholars.lib.ntu.edu.tw/handle/123456789/342622https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032141817&partnerID=40&md5=944ea2abb3036cfbeb07b992a0d9fbd1This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible. key words:.Adaptive noise control (anc); Backpropagation algorithm; Multilayer perceptrons (mlps); Neural networkAdaptive algorithms; Backpropagation; Computer software; Digital signal processing; Magnetic resonance imaging; Multilayer neural networks; Signal filtering and prediction; Spurious signal noise; Backpropagation algorithm; Broadband active noise; Adaptive control systemsjournal article2-s2.0-0032141817WOS:000075611000012