A neural networks learning algorithm for minor component analysis and its convergence analysis

Peng, Dezhong, Yi, Zhang, Lv, JianCheng and Xiang, Yong 2008, A neural networks learning algorithm for minor component analysis and its convergence analysis, Neurocomputing, vol. 71, no. 7-9, pp. 1748-1752.

Attached Files
Name Description MIMEType Size Downloads

Title A neural networks learning algorithm for minor component analysis and its convergence analysis
Author(s) Peng, Dezhong
Yi, Zhang
Lv, JianCheng
Xiang, Yong
Journal name Neurocomputing
Volume number 71
Issue number 7-9
Start page 1748
End page 1752
Publisher Elsevier BV
Place of publication Amsterdam, Netherlands
Publication date 2008-03
ISSN 0925-2312
1872-8286
Keyword(s) minor component analysis (MCA)
deterministic discrete time (DDT) system
eigenvalue
eigenvector
Summary The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.
Language eng
Field of Research 090609 Signal Processing
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2008
Copyright notice ©2007, Elsevier B.V
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017545

Document type: Journal Article
Collection: School of Engineering and Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 2 times in TR Web of Science
Scopus Citation Count Cited 4 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 399 Abstract Views, 5 File Downloads  -  Detailed Statistics
Created: Fri, 14 Aug 2009, 13:54:29 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.