ON DUALITY OF REGULARIZED EXPONENTIAL AND LINEAR FORGETTING

Regularized (stabilized) versions of exponential and linear forgetting in parameter tracking are shown to be dual to each other. Both are de rived by solving basically the same Bayesian decision problem where Ku llback-Leibler divergence is used to measure (quasi)distance between p robability distributions of estimated parameters. The type of forgetti ng depends solely on the order of arguments in Kullback-Leibler diverg ence. This general view indicates under which conditions one technique is superior to the other. Applied to the case of ARX models, the appr oach results in a class of regularized or stabilized forgetting strate gies that are naturally robust with respect to poor system excitation. Copyright (C) 1996 Elsevier Science Ltd.