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Developing Fuzzy State Models as Markov Chain Models with Fuzzy Encoding

  • Dimitar Filev
  • Ilya Kolmanovsky
  • Ronald Yager
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 326)

Abstract

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).

Keywords

Automotive applications Fuzzy systems Granular computing Markov models Possibility theory Belief functions Stochastic dynamic programming 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Research & Innovation CenterFord Motor CompanyDearbornUSA
  2. 2.Department of Aerospace EngineeringUniversity of MichiganAnn ArborUSA
  3. 3.Machine Intelligence Institute, Iona CollegeNew RochelleUSA

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