Podrobný výpis o publikaci

POKORNÝ, Miroslav, Michal MENŠÍK, Ekaterina CHYTILOVA, Dana BERNARDOVÁ a Zdeňka KRIŠOVÁ. Soft-Computing Technologies in Economics Expert Systems. In Expert Systems: Design, Applications and Technology. 2017, s. 1-58. ISBN 978-1-5361-2520-7.

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TY  - CHAP
ID  - 317239
AU  - Pokorný, Miroslav - Menšík, Michal - Chytilova, Ekaterina - Bernardová, Dana - Krišová, Zdeňka
PY  - 2017
TI  - Soft-Computing Technologies in Economics Expert Systems
SN  - 9781536125207
KW  - expert systems;The methods of calculating indicators;fuzzy arithmetic;fuzzylogic
N2  - 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.
ER  -

Základní údaje
Originální název	Soft-Computing Technologies in Economics Expert Systems
Autoři	POKORNÝ, Miroslav, Michal MENŠÍK, Ekaterina CHYTILOVA, Dana BERNARDOVÁ a Zdeňka KRIŠOVÁ.
Vydání	Expert Systems: Design, Applications and Technology, od s. 1-58, 2017.

Další údaje
Originální jazyk	angličtina
Typ výsledku	Kapitola resp. kapitoly v odborné knize
Obor	10201 Computer sciences, information science, bioinformatics
Utajení	není předmětem státního či obchodního tajemství
Organizační jednotka	Moravská vysoká škola Olomouc
ISBN	978-1-5361-2520-7
Klíčová slova anglicky	expert systems;The methods of calculating indicators;fuzzy arithmetic;fuzzylogic
Změnil	Změnila: Ing. Michaela Nováková, učo 5293. Změněno: 30. 4. 2021 13:52.

Anotace
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.

Anotace

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.