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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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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 equation, w(n+1) = w(n) + μx(n) (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 succ.................. [:=> 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,
| ||

; 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 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.................. [:=> Show Contents <=:] |

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