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There are multiple uses for the least mean square metric, and multiple algorithm using it.
But in general you look for the smallest difference between the data you have and the predictions of several models you could use to describe those data. See related link for use in adaptive filters.
"least mean square" means that you
- calculate the difference between the data value and the model prediction at several different places (this is called the error)
- square the error to make all values positive (square)
- calculate the average (mean square)
- find the model alternative that gives the smallest error (least mean square)
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When is minimum mean square error algorithm used?
This type of algorithm is commonly used in n dimensional clustering applications. This mean is commonly the simplest to use and a typical algorithm employing the minimum square error algorithm can be found in McQueen 1967.
What is BLMS?
The expansion of BLMS is Block Least Mean Square Adaptive Algorithm , it is nothing but advanced of LMS filter which is frequently used in DSP.
What is algorithm?
The Recursive least squares RLS adaptive filter is an algorithm which recursively finds the filter coefficients that minimize a weighted linear least squares cost function relating to the input signals. This is in contrast to other algorithms such as the least mean squares LMS that aim to reduce the mean square error. In the derivation of the RLS, the input signals are considered deterministic, while for the LMS and similar algorithm they are considered stochastic. Compared to most of its competitors, the RLS exhibits extremely fast convergence. However, this benefit comes at the cost of high computational complexity.
What is rls algorithm?
The Recursive least squares RLS adaptive filter is an algorithm which recursively finds the filter coefficients that minimize a weighted linear least squares cost function relating to the input signals. This is in contrast to other algorithms such as the least mean squares LMS that aim to reduce the mean square error. In the derivation of the RLS, the input signals are considered deterministic, while for the LMS and similar algorithm they are considered stochastic. Compared to most of its competitors, the RLS exhibits extremely fast convergence. However, this benefit comes at the cost of high computational complexity.
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