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## LEAST MEAN SQUARE ALGORITHMPosted 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 ,
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| ||

minimizing the error between the reference signal , which closely matches or has some extent of
correlation with the desired signal estimate and the beamformer output y(t) (equal to wx(t)). This
is a classical Weiner filtering problem for which the solution can be iteratively found using the
LMS algorithm. LMS algorithm formulation (All signals are represented by their sample values) From the method of steepest descent, the weight vector equation is given by , )})]({([21)()1(2neEnwnw−∇+=+μ (6.2) Where μ is the step-size parameter and controls the convergence chachteristics o.................. [:=> Show Contents <=:] | |||

## LEAST MEAN SQUARE ALGORITHMPosted 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,
| ||

correlation with the desired signal estimate and the beamformer output y(t) (equal to wx(t)). This
is a classical Weiner filtering problem for which the solution can be iteratively found using the
LMS algorithm. LMS algorithm formulation (All signals are represented by their sample values) From the method of steepest descent, the weight vector equation is given by , )})]({([21)()1(2neEnwnw−∇+=+μ (6.2) Where μ is the step-size parameter and controls the convergence chachteristics of the LMS algorithm; e2(n) is the mean square error between the beamformer output y(n) and the .................. [:=> Show Contents <=:] |

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