Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/34347
Title: A neural network approach to blind source separation
Researcher: Malathi D
Guide(s): Gunasekaran N
Keywords: blind source separation
information and communication engineering
neural network
Upload Date: 10-Feb-2015
University: Anna University
Completed Date: 01/02/2010
Abstract: This work has focused on development of an Adaptive Self newlineNormalized Radial Basis Function ASNRBF neural network and anUnsupervised Stochastic Gradient Descent learning Algorithm USGDA for newlineBlind Source Separation BSS problem BSS is one of the fundamental and newlinechallenging problems in Artificial Neural Networks ANN and SignalProcessing fields It is an emerging field of fundamental research with many newlinepotential applications and it has garnered much recent research andcommercial interest in the fields such as digital and wireless communications newlinesignal processing acoustics medicine etc The objective of blind sourceseparation is to separate unknown signals that have been mixed together the newlinedesired signals and the mixing matrix are not known and the only availabledata being the mixture signal Hyvarinen et al 2001 newline newline
Pagination: xv, 139p.
URI: http://hdl.handle.net/10603/34347
Appears in Departments:Faculty of Information and Communication Engineering

Files in This Item:
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01_title.pdfAttached File30.18 kBAdobe PDFView/Open
02_certificate.pdf20.57 kBAdobe PDFView/Open
03_abstract.pdf25.66 kBAdobe PDFView/Open
04_acknowledgement.pdf20.3 kBAdobe PDFView/Open
05_contents.pdf47.43 kBAdobe PDFView/Open
06_chapter 1.pdf200 kBAdobe PDFView/Open
07_chapter 2.pdf188.72 kBAdobe PDFView/Open
08_chapter 3.pdf869.73 kBAdobe PDFView/Open
09_chapter 4.pdf237.35 kBAdobe PDFView/Open
10_chapter 5.pdf82.35 kBAdobe PDFView/Open
11_appendix.pdf88.61 kBAdobe PDFView/Open
12_references.pdf30.48 kBAdobe PDFView/Open
13_publications.pdf21.08 kBAdobe PDFView/Open
14_vitae.pdf17.33 kBAdobe PDFView/Open


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