Incremental regularization to compensate biased teachers in incremental learning

Learning control for complex technical systems needs a suitable trade-off between requiring little modelling efforts, fast learning and safety considerations. Incremental learning by the Directed Self-Learning strategy seems to be a good candidate for practical purposes. The learning stimuli are given incrementally by a law of adaptation acting as a teacher. But this teacher may be biased in several ways. This paper indicates that such a situation of biased teachers in incremental learning can be compensated by regularization. But in this context, regularization has to be incremental. Such an incremental regularization scheme is formally analyzed in order to extract engineering and design guidelines. The scheme is then demonstrated in a simulation setup of incremental function approximation with different biased teachers and compared to the cerebellar modelling articulation controller (CMAC).