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Automatic Control and Computer Sciences

Erschienen in:

01.11.2020

Modelling of Knowledge via Fuzzy Knowledge Unit in a Case of the ERP Systems Upgrade

verfasst von: Michal Peták, Helena Brožová, Milan Houška

Erschienen in: Automatic Control and Computer Sciences | Ausgabe 6/2020

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Abstract

This article is addressed to knowledge modelling and formalization using a fuzzified knowledge unit. The work is based on the system approach to the definition of knowledge units, on the procedural form of knowledge. Fuzzification of knowledge units draws innovation potential from knowledge units with fuzzy linguistic variables and Mamdani fuzzy inference system. Fuzzy knowledge units arise as a join the best properties of the given approaches. The core is the knowledge unit itself comprising the description of a problem and its solution. The typical knowledge unit consists of four elements – X as problem situation, Y as elementary problem, Z as goal of elementary problem solving and Q as solution of elementary problem. A last element of a knowledge unit Q is fuzzified by fuzzy linguistic variable. Steps of fuzzification process are described in the case study “Process customization.” The discussion unifies the findings from the chapters with results of the case study.

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Abbasbandy, S. and Hajjari, T., A new approach for ranking of trapezoidal fuzzy numbers, Comput. Math. Appl., 2009, vol. 57, no. 3, pp. 413–419. https://doi.org/10.1016/j.camwa.2008.10.090MathSciNetCrossRefMATH

Brožová, H. and Houška, M., Knowledge Modelling, Prague: Prof. Publ., 2011.

Dömeová, L., Houška, M., and Beránková Houšková, M., Systems Approach to Knowledge Modelling, Hradec Králové: Graphical Studio Olga Čermáková, 2008.

Goetschel, R. and Voxman, W., Elementary fuzzy calculus, Fuzzy Sets Syst., 1986, vol. 18, no. 1, pp. 31–43. https://doi.org/10.1016/0165-0114(86)90026-6MathSciNetCrossRefMATH

Casillas, J., Cordon, O., and Herrera, F., Accuracy Improvements in Linguistic Fuzzy Modeling, Berlin–Heidelberg: Springer-Verlag GmbH & Co, 2003.CrossRef

Houška, M. and Beránková, M., Binary Operations with Knowledge Units, AWER Procedia Inf. Technol. Comput. Sci., 2013, no. 3, pp. 1716–1726.

Kendal, S.L. and Creen, M., An Introduction to Knowledge Engineering, London: Springer, 2007.MATH

Lilly, J.H., Fuzzy Control and Identification, New Jersey: John Wiley & Sons, Inc., 2010.CrossRef

Mamdani, E.H. and Assilian, S., An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man-Mach. Stud., 1975, vol. 7, pp. 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2CrossRefMATH

10.

Mareš, M., Fuzzy sets, Scholarpedia, 2006, vol. 1, no. 10, p. 2031.https://doi.org/10.4249/scholarpedia.2031.ISSN1941-6016

11.

Moreno, C.J., and Espejo, E., A performance evaluation of three inference engines as expert systems for failure mode identification in shafts, Eng. Failure Anal., 2015, vol. 53, pp. 24–35. https://doi.org/10.1016/j.engfailanal.2015.03.020CrossRef

12.

Novák, V., Perfilieva, I., and Dvořák, A., Insight into Fuzzy Modeling, New Jersey: John Wiley & Sons, Inc., 2016.CrossRef

13.

Oliinyk, A., Skrupsky, S., Subbotin, S., and Korobiichuk, I., Parallel method of production rules extraction based on computational intelligence, Autom. Control Comput. Sci., 2017, vol. 51, no. 4, pp. 215–223.CrossRef

14.

Peták, M. and Houška, M., Fuzzy knowledge unit, in 12th International Scientific Conference on Distance Learning in Applied Informatics, Štúrovo, Slovakia, 2018, pp. 491–502.

15.

Peták, M. and Houška, M., An inference strategy for knowledge units, HAICTA 2017: 8th International Conference on Information & Communication Technologies in Agriculture, Food and Environment, Chania, Crete, Greece, 2017, vol. 2030, pp. 304–313. http://ceur-ws.org/Vol-2030/.

16.

Rauchová, T. and Houška, M., Efficiency of knowledge transfer through knowledge texts: Statistical analysis, J. Effic. Responsib. Educ. Sci., 2013, no. 6, pp. 46–60. https://doi.org/10.1016/j.sbspro.2013.12.003

17.

Sugiyama, K. and Meyer, B.J., Knowledge process analysis: Framework and experience, J. Syst. Sci. Syst. Eng., 2008, vol. 17, no. 1, pp. 86–108.CrossRef

18.

Venturelli, A., Caputo, F., Leopizzi, R., Mastroleo, G., and Mio, Ch., How can CSR identity be evaluated? A pilot study using a Fuzzy Expert System, J. Cleaner Prod., 2017, vol. 141, no. 1000–1010. https://doi.org/10.1016/j.jclepro.2016.09.172

Titel: Modelling of Knowledge via Fuzzy Knowledge Unit in a Case of the ERP Systems Upgrade
verfasst von: Michal Peták
Helena Brožová
Milan Houška
Publikationsdatum: 01.11.2020
Verlag: Pleiades Publishing
Erschienen in: Automatic Control and Computer Sciences / Ausgabe 6/2020
Print ISSN: 0146-4116
Elektronische ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411620060061

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