Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Filtro de Wiener× | Filtro Adaptativo LMS× | |
|---|---|---|
| Campo | Procesamiento de señales | Procesamiento de señales |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1949 | 1960 |
| Autor original≠ | Norbert Wiener | Bernard Widrow and Marcian E. Hoff |
| Tipo≠ | Linear mean-square optimal filter | Gradient descent adaptive filtering |
| Fuente seminal≠ | Wiener, N. (1949). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. John Wiley & Sons. link ↗ | Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits. IRE Wescon Convention Record, 4, 96–104. link ↗ |
| Alias | Wiener Optimal Filter, Kolmogorov-Wiener Filter, Mean-Square Optimal Filter | LMS Filter, Adaptive LMS Algorithm, Gradient Descent Filtering |
| Relacionados | 4 | 4 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
|
|