Recursive Least Squares Filter
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,
Read more about Recursive Least Squares Filter: Motivation, Discussion, Recursive Algorithm, RLS Algorithm Summary, Lattice Recursive Least Squares Filter (LRLS), Normalized Lattice Recursive Least Squares Filter (NLRLS)
Famous quotes containing the word squares:
“And New York is the most beautiful city in the world? It is not far from it. No urban night is like the night there.... Squares after squares of flame, set up and cut into the aether. Here is our poetry, for we have pulled down the stars to our will.”
—Ezra Pound (1885–1972)