Least Mean Squares Algorithm confusion with Adaptive Line Enhancement

1. What is LMS adaptive filter?
2. What are the factors that determine the performance of an adaptive algorithm?
3. How does LMS algorithm work?
4. What is normalized LMS?

What is LMS adaptive 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 square of the error signal (difference between the desired and the actual signal).

What are the factors that determine the performance of an adaptive algorithm?

The performance of different adaptive filter algorithms are decided based on the following factors: (1) Elapsed time and (2) Mean Square Error (MSE). Content may be subject to copyright. Elapsed time and (2) Mean Square Error (MSE).

How does LMS algorithm work?

LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error.

What is normalized LMS?

The NLMS processing functions accept the input and reference input signals and generate the filter output and error signal. Internal structure of the NLMS adaptive filter. The functions operate on blocks of data and each call to the function processes blockSize samples through the filter.