Developing Fuzzy State Models as Markov Chain Models with Fuzzy Encoding

This paper examines the relationship and establishes the equivalence between a class of dynamic fuzzy models, called Fuzzy State Models (FSM), and recently introduced Markov Chain models with fuzzy encoding. The equivalence between the two models leads to a methodology for learning FSMs from data and a systematic way for model based design of rule-based fuzzy controllers. The proposed approach is demonstrated on a case study of vehicle adaptive cruise control system in which an FSM is identified from simulation data and a fuzzy feedback controller is generated by exploiting the Stochastic Dynamic Programming (SDP).

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