Probabilities from fuzzy observations
Information Sciences: an International Journal
Fuzziness and loss of information in statistical problems
IEEE Transactions on Systems, Man and Cybernetics
Asymptotically attainable structures in nonhomogeneous Markov systems
Operations Research
The theory of fuzzy stochastic processes
Fuzzy Sets and Systems
Fuzzy time series and its models
Fuzzy Sets and Systems
Fuzzy subsets of the space of probability measures and expected value of fuzzy variable
Fuzzy Sets and Systems
Convergence of sequences of fuzzy random variables and its application
Fuzzy Sets and Systems
A comparison of fuzzy forecasting and Markov modeling
Fuzzy Sets and Systems
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy Markovian decision process
Fuzzy Sets and Systems
Fuzzy Non-Homogeneous Markov Systems
Applied Intelligence
Fuzzy relation equations and fuzzy inference systems: an insideapproach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy Non-Homogeneous Markov Systems
Applied Intelligence
An Improvement of Coleman Model of Trust: Using FNHMS to Manage Reputation in Multi-agent Systems
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Fuzzy semi-Markov migration process in credit risk
Fuzzy Sets and Systems
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In this paper the theory of fuzzy logic and fuzzy reasoning is combined with the theory of Markov systems and the concept of a fuzzy non-homogeneous Markov system is introduced for the first time. This is an effort to deal with the uncertainty introduced in the estimation of the transition probabilities and the input probabilities in Markov systems. The asymptotic behaviour of the fuzzy Markov system and its asymptotic variability is considered and given in closed analytic form. Moreover, the asymptotically attainable structures of the system are estimated also in a closed analytic form under some realistic assumptions. The importance of this result lies in the fact that in most cases the traditional methods for estimating the probabilities can not be used due to lack of data and measurement errors. The introduction of fuzzy logic into Markov systems represents a powerful tool for taking advantage of the symbolic knowledge that the experts of the systems possess.