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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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]LEAST MEAN SQUARE ALGORITHMIntroduction The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 is an adaptive algorithm, which uses a gradient-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 rela.................. [:=> 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,
| ||

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 r and R instead of their actual values i.e. R(n) = x(n)xh(n) (6.5) r(n) = d*(n)x(n) (6.6) Therefore the weight update can be given by the following equa.................. [:=> Show Contents <=:] |

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