Titlebook: Combining Fuzzy Imprecision with Probabilistic Uncertainty in Decision Making; Janusz Kacprzyk,Mario Fedrizzi Book 1988 Springer-Verlag Be

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Fuzzy Dynamic Programming with Stochastic Systemslems, fuzzy dynamic programming for the case of a deterministic and fuzzy system under control is provided. Then, the case of a stochastic system under control is discussed in detail. Two formulations are shown: first, the classic one due to Bellman and Zadeh (1970) in which an optimal sequence of c

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0075-8442 vestment in a company engaged in exploration and marketing of oil? As another example, consider a collection of rules of the form "If X is Ai then Y is B,," j =978-3-540-50005-6978-3-642-46644-1Series ISSN 0075-8442 Series E-ISSN 2196-9957

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fleeting

Essay on the History of the Development of Many-Valued Logics and Some Related Topics

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Combining Fuzzy Imprecision with Probabilistic Uncertainty in Decision Making

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Alpha-Cells

https://doi.org/10.1007/978-3-642-37181-3In this paper we examine various problems of statistical inference under the presence of vagueness. More specifically, we discuss the problem of confidence estimation where the available data are imprecise. We indicate some possible applications.

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Kristin Eissfeldt,Christa JaegerThis paper discusses the probabilistic structure in the problem of combination of evidence. We propose to use the theory of random sets as a general framework for combining various types of uncertainty measures such as probability measures, belief functions and possibility distributions.

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Mammal

https://doi.org/10.1007/978-3-8349-8840-9Stochastic fuzzy sets combine both the probabilistic and the fuzzy measures. The theoretical foundations have been continuously developing over the last two decades. They were observed in empirical measurement studies. They were instrumental in the formulation of complex control models associated with multi-stage decision problems.