INFORMATION MODELING FUZZY KNOWLEDGE
DOI:
https://doi.org/10.15588/1607-3274-2018-4-8Keywords:
information model, fuzzy knowledge, fuzzy sets, membership function, expert judgment, decision making.Abstract
Context. The research of the actual problem of the development of information models for the presentation of fuzzy knowledge for information technologies has been carried out on the example of various applied problems that occur during the functioning of socioeconomic
systems with the use of fuzzy sets, fuzzy logic and system approach.
The purpose of this work is the development of information models for the presentation of fuzzy knowledge for the adoption of managerial decisions in the functioning of socio-economic systems in the conditions of uncertainty for incoming expert assessments.
Objective. The object of the research is the process of modeling fuzzy knowledge based on membership functions for incoming expert evaluations according to the criteria.
The subject of the research is the methods and models of presentation of fuzzy knowledge for making decisions in conditions of uncertainty.
Method. For the first time a representation information modeling fuzzy knowledge based functions of assessments on the criteria and their possible use for different applications. The model representation of fuzzy knowledge for evaluating the solvency of enterprises and
investment projects, forming a set of evaluation criteria and examples of constructions membership functions for comparing input. For the first time a representation of the information model of fuzzy knowledge input to expert estimates, the example of a startup evaluation of
projects that will provide linguistic value and reliability assessment of alternatives.
Results. The result of the study is the information modeling of the presentation of fuzzy knowledge on examples of construction of models for assessing the solvency of enterprises, investment and startup projects on incoming expert assessments. The developed model gives an opportunity for the recruited expert points of a weakly structured or unstructured task to receive interpretations, revealing the subjectivity of experts and having a quantitative assessment in non-formalized problems. The rationality of the assessment proves the advantages of the developed models.
Conclusions. The scientific and applied task of developing informational models of presentation of fuzzy knowledge for information technology is solved in the work on examples of construction of models of solvency assessment of enterprises, investment projects and startup
of projects according to incoming expert assessments. The development of models of fuzzy knowledge will provide an opportunity to adequately approach the evaluation of alternative solutions, while increasing the degree of validity of decision-making. The proposed information
models of fuzzy knowledge of the assessment of enterprises’ solvency, investment and startup projects can be implemented into the work of investment institutions.
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