: Coverage of Wiener filters , Linear Prediction , and the Method of Steepest Descent .
This paper evaluates the performance of the Least-Mean-Square (LMS) and Recursive Least-Squares (RLS) algorithms under conditions where signal characteristics change faster than the filter’s convergence rate. We examine the trade-offs between computational simplicity and tracking accuracy. 2. Introduction
The text explores how filters use feedback—often an error signal—to refine their transfer functions and minimize cost functions, typically the . Key algorithms and concepts covered include:
Furthermore, the mathematical machinery in Haykin (linear algebra, stochastic gradients, optimal estimation) is directly transferable to the core of modern machine learning—specifically, online learning, reinforcement learning (TD-learning is a form of adaptive filtering), and optimization theory.