## estimate the mean vector and the covariance matrixis hidden..!!Click Here to show estimate the mean vector and the covariance matrix's more details.. | |||

Do You Want To See More Details About "estimate the mean vector and the covariance matrix" ? Then ## .Ask Here..!with your need/request , We will collect and show specific information of estimate the mean vector and the covariance matrix's within short time.......So hurry to Ask now (No Registration , No fees ...its a free service from our side).....Our experts are ready to help you...## .Ask Here..! | |||

In this page you may see estimate the mean vector and the covariance matrix related pages link And You're currently viewing a stripped down version of content. open "Show Contents" to see content in proper format with attachments | |||

Page / Author | tags | ||

## 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 ,
| ||

= 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 equation, w(n+1) = w(n) + μx(n)[d*(n) – xh(n)w(n) .................. [:=> 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,
| ||

ented by their sample values) From the method of steepest descent, the weight vector equation is given by , )})]({(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 in.................. [:=> Show Contents <=:] |

Cloud Plugin by Remshad Medappil |