Abstract:
Fuzzy discrete event systems (FDESs) can model event-driven systems as fuzzy automata with ambiguous states and event-invoked state transitions. In two recent papers, we developed algorithms for online-supervised learning of the fuzzy automaton’s event transition matrix using fuzzy states before and after fuzzy events. Pre- and post-event states were either directly available or estimable through learning. This article develops algorithms for learning the transition matrix when only the pre-event state is available. A fuzzy set linked to a physical variable describes the post-event state. Stochastic-gradient-descent algorithms learn the transition matrix and parameters of Gaussian fuzzy sets. Computer simulations verify theoretical development.
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