Recursive Least Squares (RLS)

Recursive Least Squares (RLS) is an adaptive filtering technique used to estimate unknown parameters in a model over time. Unlike traditional methods that require all data to be processed at once, RLS updates estimates incrementally with new data as it arrives. This allows for fast adjustments to changes in patterns, making RLS particularly useful in real-time applications like signal processing, control systems, and machine learning. By minimizing the difference between predicted and actual outcomes, RLS continuously refines its model to improve accuracy without needing to store all past information.