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Weiner and LMS both are adaptive filters and doing same thing. Both are stochastic based.
In weiner we need R Matrix (Correlation of input) and P Matrix (Cross Correlation of Input and output) and W=Inv(R) * P
So in wiener filter we need only R and P Matrix.
But In LMS are are minimising the MSE (Mean Square Error) It involves iteration. LMS is normaly used for adaptive filter.
Because in Wiener we need to compute inverse, it is not easy in hardware to implement. so we implement LMS
The Wiener filtering usually executes an optimal tradeoff between inverse filtering and noise smoothing. https://www.owlnet.rice.edu/~elec539/Projects99/BACH/proj2/wiener.html
This problem, strictly speaking, has nothing in common with LMS, as it is used for restoration of original (input) signal, not estimation of transfer function
Weiner and LMS both are stochastic based filters. If we do not consider E[.] operator in Wiener then we will get an iterative form LMS of Wiener. So we can do same thing just by implementing an iterative Algo rather than implementing wiener filter which requires computation of inv(R)
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