NORMALIZED TIME VARYING MIXED NORM LMS-LMF ADAPTIVE ALGORITHM

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" NORMALIZED TIME VARYING MIXED NORM LMS-LMF ADAPTIVE ALGORITHM "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	151217
Doc. No	:	ET23009
Main Entry	:	MUHAMMAD MOINUDDIN
Title Proper	:	NORMALIZED TIME VARYING MIXED NORM LMS-LMF ADAPTIVE ALGORITHM
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	The least-mean squaTe (LMS) and least-mean fourth (LMF) algorithms are twoimportant adaptive schemes. They have several advantages and disadvantages. Theseare combined in one which i s named as mixed-norm algorithm i n order t o utilize thebenefits of both the algorithms in which mizing parameter i s fixed. Recently a newweighted sum of LMS and LMF algorithm has been proposed in which the mixingparameter i s time varying and has the ability t o adapt the variations in the envi-ronment. Due t o its dependence o n LMS and LMF algorithms, it i s eflected by thehigh eigen value spread of the input sequence. In order to overcome this problem, anormalized time varying mixed norm LMS-LMF algorithm i s proposed i n this thesiswork. Convergence analysis for the mean and the mean square of the proposed algo-rithm are presented in this work and the bound for the convergence in the mean 2s alsoderived. Steady state analysis i s carried out and the expressions for the steady-stateexcess mean-square error are derived for both NLMS and the proposed algorithms.Tracking analysis is also presented i n the presence of two type of nonstatzonarities,random and cyclic. Also, the expressions for the corresponding steady-state excessmean-square error are derived for both algorithms,-....,....-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL46238.pdf
Title and statement of responsibility and	:	NORMALIZED TIME VARYING MIXED NORM LMS-LMF ADAPTIVE ALGORITHM [Thesis]