Soft computing approach to adaptive noise filtering

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2004 IEEE Conference on Cybernetics and Intelligent Systems
Pages1-5
Number of pages5
StatePublished - 2004
Event2004 IEEE Conference on Cybernetics and Intelligent Systems - , Singapore
Duration: 01 12 200403 12 2004

Publication series

Name2004 IEEE Conference on Cybernetics and Intelligent Systems

Conference

Conference2004 IEEE Conference on Cybernetics and Intelligent Systems
Country/TerritorySingapore
Period01/12/0403/12/04

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