FUZZY MODELING OF VERBAL INFORMATION FOR PRODUCTION SYSTEMS
DOI:
https://doi.org/10.30837/2522-9818.2019.10.005Keywords:
membership function, ratio of order, linguistic variableAbstract
The subject of the article's research is the formalization of unstructured or semistructured verbal information for a fuzzy production system. The purpose of the work is to develop a method of constructing membership functions for fuzzy sets of terms of a linguistic variable that will allow formalizing unstructured or semistructured verbal information for fuzzy production systems. The following tasks are solved in the article: to develop a method of constructing the membership functions to determine the sequence of stages: the stage of modeling verbal information in the form of digraphs, the stage of constructing the order relation on the elements of this model, the step of determining a linguistic variable based on the created model, and determining the functions of fuzzy sets of linguistic terms variable. Methods are used: graph theory, mathematical induction, fuzzy modeling. Results obtained: a method for constructing the membership function of linguistic variables that formalizes unstructured or semi-structured qualitative information for fuzzy production systems is developed. For this purpose, the process of constructing the membership function has been broken down into stages. The implementation of the first stage requires the creation of a model of unstructured or semistructured verbal information. Three models of information based on oriented trees are considered with increasing complexity. A model based on an acyclic oriented graph is considered as a generalization. Such a model is the basis for processing information that has a structure of greater complexity. The second stage provides a theoretical basis for constructing the order relation for the elements of the models under consideration. For the implementation of the third stage, a method of identifying the order elements on the basis of the positional system is proposed. Based on the ID of each ordered element, functions of fuzzy sets of terms of a linguistic variable are constructed. Appropriate procedures have been developed to implement the steps. Conclusions: application of the method will allow automating the assignment of vectors of input and output information, to automate the formation of fuzzy sets of terms of the corresponding linguistic variables, will allow to build fuzzy products as a knowledge base of a fuzzy production system, and to train such a system.
References
Konar, A. (2018), Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain, CRC press, 787 p. DOI: https://doi.org/10.1201/9781420049138
Stovba, S. D. (2007), Design of fuzzy systems by means of Matlab, Hotline-telecom,Moscow, 288 p.
Borisov, A. N., Krumberg, O. A., Fyodorov, I. P. (1990), Decision-making on the basis of fuzzy models: Examples of using, Zinatne, Riga, 184 p.
Novák, V., Perfilieva, I., Mockor, J. (1999), Mathematical Principles of Fuzzy Logic, Springer Science & Business Media, 320 p. DOI: https://doi.org/10.1007/978-1-4615-5217-8
Samohvalov, Yu Y. (2017), "Assessment of validity of management decisions on the basis of fuzzy logic", Control systems and machines, No. 3, P. 26–34. DOI: https://doi.org/10.15407/usim.2017.03.026
Bakurova, A. V., Ivanov, V. N. (2013), "Method for constructing the function of normalizing termsets to display the membership function of the operational risk assessment", Economical cybernetics, No. 4-6 (82-84), P. 40–43.
Borisov, V. V., Fedulov, A. S., Zernov, M. M. (2014), Foundations of fuzzy set theory, Moscow, 98 p.
Kolchev, N. G., Berstein, L. S., Bozhenyuk, A. V. (1991), Fuzzy models for expert systems in CAD, Moscow, 136 p.
Pegat, A. (2013), Fuzzy modeling and management,Moscow, 798 p.
Aracil, J., Garcia-Cerezo, A., Ollero, A. (1991), "Fuzzy control of dynamical systems. Stability analysis based on the conicity criterion", Proceedings of the 4th Iternational Fuzzy Systems Association Congress, Brussels, Belgium, P. 5–8.
Turskis, Z., Zavadskas, E. K., Antucheviciene, J., Kosareva, N. A. (2015), "Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection", International Journal Of Computers Communications & Control Special Issue On Fuzzy Sets And Applications (Celebration Of The 50th Anniversary Of Fuzzy Sets), No. 10 (6), P. 873–888. DOI: https://doi.org/10.15837/ijccc.2015.6.2078
Saaty T. L., Vargas, L. G. (2011), "The possibility of group choice: Pairwise comparisons and merging functions", Social Choice and Welfare, No. 38 (3), P. 481–496. DOI: https://doi.org/10.1007/s00355-011-0541-6
Ozdemir, M., Saaty, T. L. (2006), "The unknown in decision making: What to do about it", European Journal of Operational Research, No. 174, P. 349 – 359. DOI: https://doi.org/10.1016/j.ejor.2004.12.017
Gnatiyenko, G. M., Snityuk, V. E. (2008), Expert technologies of decision-making,Kiev, 444 p.
Bernard, H., Wutich, A, Ryan, G. (2007), Analyzing qualitative data: Systematic approaches, SAGE publications. 456 p. DOI: https://doi.org/10.4135/9781849208826.n10
Rutkovskaya, D., Pilinsky, M., Rutkovsky, L. (2004), Neural networks, genetic algorithms and indistinct systems,Moscow, 452 p.
Louviere, J. J., Flynn, T. N., Marley, A. (2015), Best-worst scaling: Theory, methods and applications, Cambridge, UK, 342 p. DOI: https://doi.org/10.1017/CBO9781107337855
Hair, Jr., Black, J., Babin, B.,Anderson, R. (2010), Multivariate data analysis: A global perspective,Upper Saddle River,NJ : Pearson Prentice Hall, 739 p. DOI: https://doi.org/10.1007/978-3-030-06031-2_16
Ore, O. (1980), Theory of graphs,Moscow, 336 p.
Taran, T.A. (2003), Foundations of Discrete Mathematics,Kiev, 288 p.
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