Tapped Delay Neural Network for Active Noise Control
碩士 === 國立中興大學 === 機械工程學系所 === 94 === Tapped delay neural network (TDNN) for active noise control is investigated in this thesis. TDNN can be used to represent a model of a secondary path in an active noise control (ANC) system. TDNN can also be used as an ANC controller to attenuate noise in the ANC...
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ndltd-TW-094NCHU53110192016-05-25T04:14:50Z http://ndltd.ncl.edu.tw/handle/92006816328074271172 Tapped Delay Neural Network for Active Noise Control 狹帶延遲類神經網路於主動噪音控制之應用 Jing-Yi Huang 黃靜宜 碩士 國立中興大學 機械工程學系所 94 Tapped delay neural network (TDNN) for active noise control is investigated in this thesis. TDNN can be used to represent a model of a secondary path in an active noise control (ANC) system. TDNN can also be used as an ANC controller to attenuate noise in the ANC system. A filtered-x back propagation (FXBP) algorithm is utilized for TDNN in ANC applications and generally leads to diverge. To overcome this difficulty, two approaches are considered. One approach applies a modified residual error for FXBP. Another approach applies a robustly controlled secondary path for TDNN. Results of computer simulation and experiment show that our proposed approaches can effectively improve the convergence property of FXBP algorithm for TDNN in ANC applications and result in good performance of noise reduction. 林忠逸 2006 學位論文 ; thesis 71 zh-TW |
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碩士 === 國立中興大學 === 機械工程學系所 === 94 === Tapped delay neural network (TDNN) for active noise control is investigated in this thesis. TDNN can be used to represent a model of a secondary path in an active noise control (ANC) system. TDNN can also be used as an ANC controller to attenuate noise in the ANC system. A filtered-x back propagation (FXBP) algorithm is utilized for TDNN in ANC applications and generally leads to diverge. To overcome this difficulty, two approaches are considered. One approach applies a modified residual error for FXBP. Another approach applies a robustly controlled secondary path for TDNN. Results of computer simulation and experiment show that our proposed approaches can effectively improve the convergence property of FXBP algorithm for TDNN in ANC applications and result in good performance of noise reduction.
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林忠逸 |
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林忠逸 Jing-Yi Huang 黃靜宜 |
author |
Jing-Yi Huang 黃靜宜 |
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Jing-Yi Huang 黃靜宜 Tapped Delay Neural Network for Active Noise Control |
author_sort |
Jing-Yi Huang |
title |
Tapped Delay Neural Network for Active Noise Control |
title_short |
Tapped Delay Neural Network for Active Noise Control |
title_full |
Tapped Delay Neural Network for Active Noise Control |
title_fullStr |
Tapped Delay Neural Network for Active Noise Control |
title_full_unstemmed |
Tapped Delay Neural Network for Active Noise Control |
title_sort |
tapped delay neural network for active noise control |
publishDate |
2006 |
url |
http://ndltd.ncl.edu.tw/handle/92006816328074271172 |
work_keys_str_mv |
AT jingyihuang tappeddelayneuralnetworkforactivenoisecontrol AT huángjìngyí tappeddelayneuralnetworkforactivenoisecontrol AT jingyihuang xiádàiyánchílèishénjīngwǎnglùyúzhǔdòngzàoyīnkòngzhìzhīyīngyòng AT huángjìngyí xiádàiyánchílèishénjīngwǎnglùyúzhǔdòngzàoyīnkòngzhìzhīyīngyòng |
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