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This document discusses adaptive filtering and summarizes key algorithms. It introduces the concept of adaptive filtering using a noise cancellation example. It then covers the Wiener filter and least mean squares algorithm for adaptive filtering. The document describes how the LMS algorithm is derived using the steepest descent method and how it is implemented in a sample-based manner. It also summarizes the recursive least squares algorithm, which converges faster than LMS. Finally, it lists several appli

Author

dhurgham zwaid

Language

EN