Soft computing approach to adaptive noise filtering

Chunshien Li*, Kuo Hsiang Cheng, Chih Ming Chen, Jin Long Chen

*此作品的通信作者

研究成果: 圖書/報告稿件的類型會議稿件同行評審

3 引文 斯高帕斯(Scopus)

摘要

A soft computing filtering approach is proposed for adaptive noise cancellation. The goal of noise cancellation is to extract the desired signal from its noise-corrupted version, using the proposed neuro-fuzzy system (NFS) as an adaptive filter. Traditional linear filtering may not be good enough to handle with the noise complexity. In the study, the NFS filter is trained in hybrid way using the well-known random optimization (RO) method and the least squares estimate (LSE) method for the noise canceling problem. The premises and the consequents of the NFS are updated for their parameters using the RO and the LSE, respectively. With the hybrid learning algorithm, the proposed approach has moderate computation and the training of the NFS filter is fast convergence. An example of noise cancellation by the proposed adaptive NFS filter is illustrated and the result is discussed. The NFS filter has stable filtering performance for noise cancellation.

原文英語
主出版物標題2004 IEEE Conference on Cybernetics and Intelligent Systems
頁面1-5
頁數5
出版狀態已出版 - 2004
事件2004 IEEE Conference on Cybernetics and Intelligent Systems - , 新加坡
持續時間: 01 12 200403 12 2004

出版系列

名字2004 IEEE Conference on Cybernetics and Intelligent Systems

Conference

Conference2004 IEEE Conference on Cybernetics and Intelligent Systems
國家/地區新加坡
期間01/12/0403/12/04

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