Recursive Least Squares
Recursive Least Squares (RLS) is an adaptive filtering algorithm used for online estimation of parameters in linear models, such as in system identification or adaptive control. It recursively updates parameter estimates as new data arrives, minimizing the weighted least squares error without requiring storage of all past data. This makes it computationally efficient and suitable for real-time applications where data streams continuously.
Developers should learn RLS when working on real-time signal processing, adaptive control systems, or machine learning applications that require incremental updates, such as online regression or adaptive filtering in telecommunications. It is particularly useful in scenarios with streaming data where batch processing is impractical, such as in financial modeling for time-series prediction or in robotics for adaptive trajectory tracking.