2004
DOI: 10.1007/978-3-540-30499-9_141
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Generalized Rule-Based Fuzzy Cognitive Maps: Structure and Dynamics Model

Abstract: Abstract. Generalized Rule-Based Fuzzy Cognitive Maps (GRFCM) are Fuzzy Cognitive Maps that use completely the fuzzy approach to the analysis and modeling of complex qualitative systems. All components (concepts, interconnections) and mechanisms (causality influence, causality accumulation, system dynamics) of the GRFCM are fuzzy. The offered dynamics model for GRFCM allows to describe and analyze essential features of complex qualitative system's behavior.

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Cited by 9 publications
(7 citation statements)
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“…Obviously, estimation of consistency levels between , and as well as setting their significance coefficients is carried out by experts. For the estimation of consistency between , and it is possible to use fuzzy cognitive model (Borisov, Fedulov 2004) and in order to set their significancethe method of pairwise comparison (Saaty 1980). Let us assume that , and correspond to the consistency level "High consistency", and the following significance coefficients are assigned to them: Consider briefly some approaches to the analysis of linguistic objects using the developed models.…”
Section: mentioning
confidence: 99%
“…Obviously, estimation of consistency levels between , and as well as setting their significance coefficients is carried out by experts. For the estimation of consistency between , and it is possible to use fuzzy cognitive model (Borisov, Fedulov 2004) and in order to set their significancethe method of pairwise comparison (Saaty 1980). Let us assume that , and correspond to the consistency level "High consistency", and the following significance coefficients are assigned to them: Consider briefly some approaches to the analysis of linguistic objects using the developed models.…”
Section: mentioning
confidence: 99%
“…For the problem being solved: Nonlinear and indirect dependencies between the factors of the models make it possible to justify the using of fuzzy cognitive models (maps) as such models [1], [2], [3], [4], [5].…”
Section: Problem Formulationmentioning
confidence: 99%
“…Let there be a set of indicators with the values that represent the results of the corresponding properties evaluation for solutions alternatives. It is required to construct the fuzzy evaluation model based on multi-level evaluation structure, various significance of indicators and compatibility relationships between indicators at each level of the model hierarchy [4,5].…”
Section: Fuzzy Evaluation Model Of Education Qualitymentioning
confidence: 99%