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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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s 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 reference signal which is given by,
e2(n) = 2 (6.3) The gradient vector in the above weight update equation can be computed as ∇(E{ew2(n)}) = - 2r + 2Rw(n) (6.4) In the method of steepest descent the biggest problem is the computation involved in finding the values r and R matrices in real time. The LMS algorithm on the other hand simplifies this by using the instantaneous values of covariance matrices .................. [:=> 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,
| ||

riance ma..................[:=> Show Contents <=:] |

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