Least Mean Squares Filter

Least Mean Squares Filter

Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual signal). It is a stochastic gradient descent method in that the filter is only adapted based on the error at the current time. It was invented in 1960 by Stanford University professor Bernard Widrow and his first Ph.D. student, Ted Hoff.

Read more about Least Mean Squares FilterIdea, Derivation, Simplifications, LMS Algorithm Summary, Convergence and Stability in The Mean, Normalised Least Mean Squares Filter (NLMS)

Other articles related to "least mean squares filter, filter":

Normalised Least Mean Squares Filter (NLMS) - Proof
... Let the filter misalignment be defined as, we can derive the expected misalignment for the next sample as Let and Assuming independence, we have The optimal learning ...

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