Tuning the forgetting factor in RLS identification algorithms

An upper bound for the parameter tracking error of RLS (recursive least squares) identification algorithms with forgetting factor (FF) is worked out in a fully stochastic framework. By minimizing such a bound, the selection of a suitable value for the FF can be performed.<>