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Removing Noise from Signals via Neural Networks

The main objective of this paper is to develop a method of removing noise from signal. This method is based on the radial-basis function networks and the principle of cross-validation in statistics. In this method, we detect noise by estimating the magnitude of validation error after training the network. Besides, this paper applies the concept of predictive coding to select data set from image when the proposed method used to deal with the noise removal problem of two-dimensional image signals. Finally, the proposed method has been employed to deal with noise removal problems of one-dimensional and two-dimensional signals. From the result of simulation, the proposed method could remove noise from signals effectively.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0801103-125450
Date01 August 2003
CreatorsZheng, Xiang-Ren
ContributorsChen-Wen Yen, Inn-chyn Her, Yih-Tun Tseng
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0801103-125450
Rightsunrestricted, Copyright information available at source archive

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