最小均方滤波器（Least Mean Square Filter，或LMS Filter）是一类可通过最小化误差信号（error signal）之均方值（mean square）而修正滤波器系数，以模拟所需理想滤波器的自适应滤波器，其中作为修正依据的误差信号为理想参考信号与实际输出信号之差。该种滤波器所用之最小均方法只以当前之信号误差值为准进行修正，是一种随机梯度下降法。最小均方法系由史丹佛大学的Bernard Widrow教授及他的首位博士班学生Marcian Hoff于1960年提出。最小均方滤波器属于常见的自适应滤波器种类之一。

Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual signal). It is a stochastic gradient descent method in that the filter is only adapted based on the error at the current time. It was invented in 1960 by Stanford University professor Bernard Widrow and his first Ph.D. student, Ted Hoff.