Titlebook: Analysis and Decision Making in Uncertain Systems; Zdzislaw Bubnicki Book 2004 Springer-Verlag London 2004 Analysis.computer.decision supp

六边形

Uncertain Logics and Variables,ncertain systems [44, 46, 54]. The uncertain variable is described by a certainty distribution given by an expert and characterizing his or her knowledge on approximate values of the variable. The uncertain variables are related to random and fuzzy variables, but there are also essential differences

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Application of Uncertain Variables,case, we assume that the unknown parameters in the function or the relation describing the plant are values of uncertain variables described by certainty distributions given by an expert. In the non-parametric case, the plant is described by the conditional certainty distribution characterizing the

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Fuzzy Variables, Analogies and Soft Variables, for random and uncertain variables. In Sect. 6.1, a very short description of fuzzy variables (see e.g. [69, 71, 74, 75, 84, 103, 104]) is given in the form needed to formulate our problems and to indicate analogies for non-parametric problems based on random, uncertain and fuzzy variables. These a

Hectic

Systems with Logical Knowledge Representation, so-called . may be used to formulate and solve the analysis and decision problems [17, 18, 19, 21–24, 26, 29]. The main idea of this method consists in replacing individual reasoning concepts based on inference rules by unified algebraic procedures based on the rules in two-value logic algebra.

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Dynamical Systems,described by traditional functional models or by relational knowledge representations. Special attention is paid to the relational plants and the descriptions based on uncertain variables [22, 54]. The considerations are completed with the optimization of a random and uncertain multistage decision p

矛盾心理

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