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LEAST MEAN SQUARE ALGORITHM


Posted 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 , last mean square algoritmen,
ignals arriving at angle of incidences)(tiiθrespectively. a(0θ) and a(i) represents the steering vectors for the desired signal and interfering signals respectively. Therefore it is required to construct the desired signal from the received signal amid the interfering signal and additional noise n(t). As shown above the outputs of the individual sensors are linearly combined after being scaled using corresponding weights such that the antenna array pattern is optimized to have maximum possible gain in the direction of the desired signal and nulls in the direction of the interferers. The weig..................[:=> Show Contents <=:]



LEAST MEAN SQUARE ALGORITHM


Posted 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 , last mean square algoritmen,
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 r and R instead of their ..................[:=> Show Contents <=:]



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