Developing Fuzzy State Models as Markov Chain Models with Fuzzy Encoding

DOI10.1007/978-3-319-19683-1_6zbMath1359.93263OpenAlexW808427616MaRDI QIDQ2971583

Ronald R. Yager, Dimitar P. Filev, Ilya V. Kolmanovsky

Publication date: 7 April 2017

Published in: Fifty Years of Fuzzy Logic and its Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-19683-1_6

zbMATH Keywords

possibility theorystochastic dynamic programmingMarkov modelsgranular computingfuzzy systemsbelief functionsautomotive applications

Mathematics Subject Classification ID

Fuzzy control/observation systems (93C42) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Fuzzy probability (60A86)

Related Items (1)

Developing Fuzzy State Models as Markov Chain Models with Fuzzy Encoding

Cites Work

• On the Dempster-Shafer framework and new combination rules
• Fuzzy modeling of complex systems
• Fuzzy dynamical systems
• Classic works on the Dempster-Shafer theory of belief functions
• Probability measures of fuzzy events
• Developing Fuzzy State Models as Markov Chain Models with Fuzzy Encoding
• Fuzzy identification of systems and its applications to modeling and control
• An experiment in linguistic synthesis with a fuzzy logic controller
• Model-Predictive Control of Discrete Hybrid Stochastic Automata
• Decision-Making in a Fuzzy Environment
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