Comparar métodos

	Filtro Adaptativo LMS	Filtro de Wiener
Campo	Procesamiento de señales	Procesamiento de señales
Familia	Process / pipeline	Process / pipeline
Año de origen	1960	1949
Autor original	Bernard Widrow and Marcian E. Hoff	Norbert Wiener
Tipo	Gradient descent adaptive filtering	Linear mean-square optimal filter
Fuente seminal	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.
Alias	LMS Filter, Adaptive LMS Algorithm, Gradient Descent Filtering	Wiener Optimal Filter, Kolmogorov-Wiener Filter, Mean-Square Optimal Filter
Relacionados	4	4
Resumen	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.