Transient probabilistic recurrent fuzzy systems
A probabilistic-based extension of recurrent fuzzy systems is presented and exemplarily applied to modeling and control of systems in different domains. The system's core-dynamic is described by a recurrent fuzzy system, while further known influencing features are summarized via probability theory using a stochastic automaton. The appropriate conditional probabilities are used to adapt the dynamics of the recurrent fuzzy system depending on its state variables. By allowing transient conditional probabilities, a time-variance is simultaneously achieved. Thus, the developed transient probabilistic recurrent fuzzy system (TP-RFS) is able to handle two kinds of uncertain information (vague and stochastic) and allows slight as well as drastic adaptations of the original recurrent fuzzy system's dynamics. Successful applications of the proposed TP-RFS for modeling different dynamics of an ecological system and for controlling the speed signaling on a highway are shown.