Online learning algorithms are needed when the process to be modeled is time varying or when it is impossible to obtain offline data that cover the whole operating region. To minimize the problems of parameter shadowing and interference, sliding-window-based algorithms are used. It is shown that, by using a sliding-window policy that enforces the novelty of the data it stores and by using a procedure to prevent unnecessary parameter updates, the performance achieved is improved over a first-in-first-out (FIFO) policy with fixed interval parameter updates. Important savings in computational effort are also obtained.