Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Filtro LMS Adattivo× | Filtro di Wiener× | |
|---|---|---|
| Campo | Elaborazione dei segnali | Elaborazione dei segnali |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1960 | 1949 |
| Ideatore≠ | Bernard Widrow and Marcian E. Hoff | Norbert Wiener |
| Tipo≠ | Gradient descent adaptive filtering | Linear mean-square optimal filter |
| Fonte seminale≠ | Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits. IRE Wescon Convention Record, 4, 96–104. link ↗ | Wiener, N. (1949). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. John Wiley & Sons. link ↗ |
| Alias | LMS Filter, Adaptive LMS Algorithm, Gradient Descent Filtering | Wiener Optimal Filter, Kolmogorov-Wiener Filter, Mean-Square Optimal Filter |
| Correlati | 4 | 4 |
| Sintesi≠ | 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. |
| ScholarGateInsieme di dati ↗ |
|
|