17-10-2017, 01:06 PM
Recursive least squares (RLS) is an adaptive filter algorithm that recursively finds coefficients that minimize a weighted linear least squares cost function related to input signals. This approach is in contrast to other algorithms such as minimum squares (LMS) that aim to reduce the mean squared error. In the derivation of the RLS, the input signals are considered deterministic, whereas for the LMS and a similar algorithm they are considered stochastic. Compared with most of its competitors, the RLS exhibits extremely fast convergence. However, this benefit is obtained at the cost of a high computational complexity.