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백프로퍼게이션의 신경회로망을 이용한 적응잡음제거기 구현

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Alternative Title
Design of the Adaptive Noise Canceler using Neural Network with Backpropagation Algorithm
Abstract
본 논문에서는 다층 신경회로망의 구조를 가지며, 백프로퍼게이션 학습 알고리즘을 이용한 적응신호처리 시스템을 구현하였다. 최소자승 알고리즘을 이용한 적응 잡음 제거기는 기준 신호와 잡음과의 상관도에 영향을 많이 받고, 신호가 잡음에 비하여 상대적으로 작은 경우에 한계를 보이고 있다. 이와 같은 잡음에 대하여 본 논문에서 제안된 시스템은 좋은 성능을 보인다. 또한, 은닉층의 수와 노드 수를 다르게 구성했을 경우에 시스템의 출력에 미치는 결과에 대하여 분석하였다. 제안된 적응 신호처리 시스템의 장점을 알아보기 위하여 성능 평가의 기준이 되는 최소자승 알고리즘을 이용한 시스템과 비교하였다.
In this paper, the adaptive noise canceler using neural network with backpropagation is designed. The adaptive noise canceler using the least mean square algorithm has the large correlativity of the reference signal and shows the limitation when the signal is relatively small to the noise. The system proposed in this paper plays an important role in denoising these signals. In addition, the experiments are carried out to analyze the effects of the number of hidden layers and nodes about the system. The performance of the proposed adaptive noise canceler is compared with that of the system which is used the least mean square algorithm.
In this paper, the adaptive noise canceler using neural network with backpropagation is designed. The adaptive noise canceler using the least mean square algorithm has the large correlativity of the reference signal and shows the limitation when the signal is relatively small to the noise. The system proposed in this paper plays an important role in denoising these signals. In addition, the experiments are carried out to analyze the effects of the number of hidden layers and nodes about the system. The performance of the proposed adaptive noise canceler is compared with that of the system which is used the least mean square algorithm.
Author(s)
추형석안종구
Issued Date
1999
Type
Research Laboratory
URI
https://oak.ulsan.ac.kr/handle/2021.oak/3796
http://ulsan.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002024182
Alternative Author(s)
Chu, Hyung SukAn, Chong Koo
Publisher
공학연구논문집
Language
kor
Rights
울산대학교 저작물은 저작권에 의해 보호받습니다.
Citation Volume
30
Citation Number
1
Citation Start Page
251
Citation End Page
264
Appears in Collections:
Research Laboratory > Engineering Research
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