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LEAST MEAN SQUARE ALGORITHM


Posted by: projectsofme
Created at: Wednesday 24th of November 2010 05:13:27 AM
Last Edited Or Replied at :Monday 18th of April 2011 01:46:46 AM
lms algorithm in mathematics , least mean squares algorithm, linear minimum mean square error algorithms doc , mathematics, mathmatics , least mean square method problems, least mean square algorithm doc , least square algorithm, seminar least mean square algorithm , estimate the mean vector and the covariance matrix, least mean squares lms algorithms , mean square error algorithm, maths seminar topics square ,
e Arrays
Consider a Uniform Linear Array (ULA) with N isotropic elements, which forms the integral part of the adaptive beamforming system as shown in the figure below. The output of the antenna arrayis given by,

tsdenotes the desired signal arriving at angle0θθandudenotes interfering signals arriving at angle of incidences)(tiiθrespectively. a(0θ) and a(i) represents the steering vectors for the desired signal and interfering signals respectively. Therefore it is required to construct the desired signal from the received signal amid the interfering signal and additional noise n(t)...................[:=> Show Contents <=:]



LEAST MEAN SQUARE ALGORITHM


Posted by: projectsofme
Created at: Wednesday 24th of November 2010 05:13:27 AM
Last Edited Or Replied at :Monday 18th of April 2011 01:46:46 AM
lms algorithm in mathematics, least mean squares algorithm , linear minimum mean square error algorithms doc, mathematics , mathmatics, least mean square method problems , least mean square algorithm doc, least square algorithm , seminar least mean square algorithm, estimate the mean vector and the covariance matrix , least mean squares lms algorithms, mean square error algorithm , maths seminar topics square,
t-based method of steepest decent . LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error. Compared to other algorithms LMS algorithm is relatively simple; it does not require correlation function calculation nor does it require matrix inversions.
6.2 LMS Algorithm and Adaptive Arrays
Consider a Uniform Linear Array (ULA) with N isotropic elements, which ..................[:=> Show Contents <=:]



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