Recursive least squares (RLS) is an adaptive filtering algorithm that updates estimates of parameters in a linear model as new data becomes available, minimizing the difference between the predicted and actual outcomes. This technique allows for real-time system identification and adaptation, making it particularly useful in dynamic environments where data is continuously changing.
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