递归最小二乘(RLS)是一种自适应滤波算法，它可以递归地找到最小化加权线性最小二乘代价函数与输入信号相关的系数。这种方法与其他算法相比较，例如最小均方(LMS)，旨在减少均方误差。在RLS的推导中，输入信号被认为是确定性的，而对于LMS和类似的算法，它们被认为是随机的。(Recursive least squares (RLS) is an adaptive filter algorithm that recursively finds the coefficients that minimize a weighted linear least squares cost function relating to the input signals. This approach 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.)