Сравнение методов

	Адаптивный фильтр LMS	Фильтр Винера
Область	Обработка сигналов	Обработка сигналов
Семейство	Process / pipeline	Process / pipeline
Год появления	1960	1949
Автор метода	Bernard Widrow and Marcian E. Hoff	Norbert Wiener
Тип	Gradient descent adaptive filtering	Linear mean-square optimal filter
Основополагающий источник	Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits. IRE Wescon Convention Record, 4, 96–104.	Wiener, N. (1949). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. John Wiley & Sons.
Другие названия	LMS Filter, Adaptive LMS Algorithm, Gradient Descent Filtering	Wiener Optimal Filter, Kolmogorov-Wiener Filter, Mean-Square Optimal Filter
Связанные	4	4
Сводка	The Least Mean Squares (LMS) filter is an adaptive signal processing algorithm that continuously updates filter coefficients to minimize the squared error between the filter output and a desired signal. Introduced by Bernard Widrow and Marcian Hoff in 1960, the LMS algorithm is one of the most widely used adaptive filtering techniques due to its simplicity, low computational cost, and ability to track time-varying signals.	The Wiener filter is an optimal linear filter that minimizes mean-square error between the desired signal and the filter output given knowledge of signal and noise statistics. Developed by Norbert Wiener in 1949, it provides the theoretical foundation for optimal filtering and remains the benchmark against which all other linear filtering methods are compared.