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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 ,
(6.7)
= w(n) + μx(n)e*(n)
The LMS algorithm is initiated with an arbitrary value w(0) for the weight vector at n=0. The successive corrections of the weight vector eventually leads to the minimum value of the mean squared error.

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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,
ct the desired signal from the received signal amid the interfering signal and additional noise n(t). As shown above the outputs of the individual sensors are linearly combined after being scaled using corresponding weights such that the antenna array pattern is optimized to have maximum possible gain in the direction of the desired signal and nulls in the direction of the interferers. The weights here will be computed using LMS algorithm based on Minimum Squared Error (MSE) criterion. Therefore the spatial filtering problem involves estimation of signalfrom the received signal (i.e. the array..................[:=> Show Contents <=:]



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