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.