POKORNÝ, Miroslav, Michal MENŠÍK, Ekaterina CHYTILOVA, Dana BERNARDOVÁ and Zdeňka KRIŠOVÁ. Soft-Computing Technologies in Economics Expert Systems. In Expert Systems: Design, Applications and Technology. 2017, p. 1-58. ISBN 978-1-5361-2520-7.
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Basic information
Original name Soft-Computing Technologies in Economics Expert Systems
Authors POKORNÝ, Miroslav, Michal MENŠÍK, Ekaterina CHYTILOVA, Dana BERNARDOVÁ and Zdeňka KRIŠOVÁ.
Edition Expert Systems: Design, Applications and Technology, p. 1-58, 2017.
Other information
Original language English
Type of outcome Chapter(s) of a specialized book
Field of Study 10201 Computer sciences, information science, bioinformatics
Confidentiality degree is not subject to a state or trade secret
Organization unit Moravian Business College Olomouc
ISBN 978-1-5361-2520-7
Keywords in English expert systems;The methods of calculating indicators;fuzzy arithmetic;fuzzylogic
Changed by Changed by: Ing. Michaela Nováková, učo 5293. Changed: 30/4/2021 13:52.
Abstract
The chapter introduces the expert systems used for the simulation of the decision-making activity of experts when dealing with complex tasks. In terms of theory, the expert knowledge method is used. Knowledge is represented in the form of IF-THEN rules. In terms of technology, the pseudo-Bayesian approaches, probability theory, theory of fuzzy set mathematics and fuzzy logic are used for the purposes of formalizing the uncertainty and inference mechanisms. The application theme is focused on decision-making in the field of economy. Supplier choice support in the supply chain using the hierarchical fuzzy-oriented expert system technology. The global decision-making task is split into partial tasks and the expert modules are integrated into a 5-level hierarchical structure for their formalization. The impact determination of selected activities of economic entities on the corporate social responsibility using the probabilistically-oriented expert system technology. The linguistic rulebased modelling and the inference method are based on the pseudo- Bayesian approach. The determination of the selected key performance indicators of Balanced Scorecard customer dimension using the fuzzystochastic oriented expert system technology. The methods of calculating indicators and standards of quality of the outputs of the systems involving the human factor do not take into account their natural uncertainty. Hence, to determine the certain performance indicator in the frame of BSC by means of fuzzification of the input statistic figures of respondents with their subsequent processing by means of fuzzy arithmetic and fuzzylogic. The efficiency of all the proposed decision-making expert systems is proven by a simulation exercise.
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