Differential Hebbian-type learning algorithms for decorrelation and independent component analysis
SCIE
SCOPUS
- Title
- Differential Hebbian-type learning algorithms for decorrelation and independent component analysis
- Authors
- Choi, SJ
- Date Issued
- 1998-04-30
- Publisher
- ELECTRONICS LETTERS
- Abstract
- Differential learning algorithms for decorrelation and independent component analysis (ICA) are presented. It is shown that the proposed differential Hebbian-type learning algorithms are able to successfully decorrelate the non-zero mean-valued data without any preprocessing Differential learning is also applied for independent component analysis (ICA) so that non-zero mean-valued source signals can be recovered without any preprocessing It is demonstrated that modified ICA algorithms using differential learning have a superior performance compared to conventional ICA algorithms for the case where the mean values of source signals are non-zero and are changing.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/37481
- DOI
- 10.1049/EL:19980636
- ISSN
- 0013-5194
- Article Type
- Article
- Citation
- ELECTRONICS LETTERS, vol. 34, no. 9, page. 300 - 301, 1998-04-30
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.