Dynamic systems are highly complex and hard to deal with due to their subject-and time-
varying nature. The fact that most of the real world systems/events are of dynamic character
makes modeling and analysis of such systems inevitable and charmingly useful. One
promising estimation method that is capable of unlearning past information to deal with non-
stationarity is Stochastic Learning Weak Estimator (SLWE) by Oommen and Rueda (2006).
However, due to using a constant learning rate, it faces a trade-off between plasticity and …

Dynamic systems are highly complex and hard to deal with due to their subject-and time-
varying na-ture. The fact that most of the real world systems/events are of dynamic character
makes modeling and analysis of such systems inevitable and charmingly useful. One
promising estimation method that is ca-pable of unlearning past information to deal with non-
stationarity is Stochastic Learning Weak Estimator (SLWE) by Oommen and Rueda (2006).
However, due to using a constant learning rate, it faces a trade-off between plasticity and …