Sign-regressor Wilcoxon and Sign-Sign Wilcoxon

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1319

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DC Field	Value	Language
dc.contributor.author	Sahoo, U K	-
dc.contributor.author	Panda, G	-
dc.contributor.author	Mulgrew, B	-
dc.date.accessioned	2010-11-10T10:53:36Z	-
dc.date.available	2010-11-10T10:53:36Z	-
dc.date.issued	2010-10	-
dc.identifier.citation	Second International Conference on Advances in Recent Technologies in Communication and Computing,ARTCom, Oct 15-16, 2010.	en
dc.identifier.uri	http://hdl.handle.net/2080/1319	-
dc.description	Copyright belongs to the Proceeding of Publisher.	en
dc.description.abstract	It is known that sign sign LMS and sign regressor LMS are faster than LMS. Inspiring from this idea we have proposed sign regressor Wilcoxon and sign-sign wilcoxon which are robust against the outlier present in the desired data and also faster than Wilcoxon and sign Wilcoxon norm. It had applied to varities of linear and nonlinear system identication problems with Gaussian noise and impulse noise present in the desired. The simulation results are compared among Wilcoxon,sign Wilcoxon and proposed sign-sign Wilcoxon and sign-regressor Wilcoxon. From simulation results it has proved that the proposed techniques are robust against outlier in the desired data and convergence speed are faster compared to other two norms.	en
dc.format.extent	323027 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.subject	Sign-regressor Wilcoxon	en
dc.subject	sign-sign Wilcoxon	en
dc.subject	sign Wilcoxon	en
dc.subject	Wilcoxon	en
dc.title	Sign-regressor Wilcoxon and Sign-Sign Wilcoxon	en
dc.type	Article	en
Appears in Collections:	Conference Papers

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