Díaz, Juan Carlos; Medina, Jesús Multi-adjoint relation equations: definition, properties and solutions using concept lattices. (English) Zbl 1320.68173 Inf. Sci. 253, 100-109 (2013). Summary: This paper generalizes fuzzy relation equations following the multi-adjoint philosophy. Moreover, the solutions of these general fuzzy relation equations and the concepts of a multi-adjoint property-oriented concept lattice are related, and several results are obtained from the theory of concept lattices.As a consequence of this relevant relation, more properties about these general equations can be proven from the theory of concept lattices and the algorithms developed to compute concept lattices can be used to obtain solutions. Furthermore, an interesting application to fuzzy logic programming has been introduced, in which an important problem in this topic has been interpreted in terms of solving a multi-adjoint relation equation. Cited in 25 Documents MSC: 68T30 Knowledge representation 03E72 Theory of fuzzy sets, etc. 06A15 Galois correspondences, closure operators (in relation to ordered sets) Keywords:fuzzy relation equation; Galois connection; property-oriented concept lattice PDF BibTeX XML Cite \textit{J. C. Díaz} and \textit{J. Medina}, Inf. Sci. 253, 100--109 (2013; Zbl 1320.68173) Full Text: DOI OpenURL References: [1] Bandler, W.; Kohout, L., Semantics of implication operators and fuzzy relational products, International Journal of Man-Machine Studies, 12, 89-116, (1980) · Zbl 0435.68042 [2] Bartl, E.; Belohlavek, R., Sup-t-norm and inf-residuum are a single type of relational equations, International Journal of General Systems, 40, 6, 599-609, (2011) · Zbl 1259.03065 [3] E. Bartl, R. Bělohlávek, J. Konecny, V. Vychodil, Isotone galois connections and concept lattices with hedges, in: 4th International IEEE Conference “Intelligent Systems”, 2008, pp. 15.24-15.28. [4] Bělohlávek, R., Lattices of fixed points of fuzzy Galois connections, Mathematical Logic Quartely, 47, 1, 111-116, (2001) · Zbl 0976.03025 [5] Bělohlávek, R., Concept equations, Journal of Logic and Computation, 14, 3, 395-403, (2004) · Zbl 1057.03021 [6] Bělohlávek, R., Concept lattices and order in fuzzy logic, Annals of Pure and Applied Logic, 128, 277-298, (2004) · Zbl 1060.03040 [7] Bělohlávek, R., Sup-t-norm and inf-residuum are one type of relational product: unifying framework and consequences, Fuzzy Sets and Systems, 197, 45-58, (2012) · Zbl 1266.03056 [8] Bělohlávek, R.; De Baets, B.; Outrata, J.; Vychodil, V., Lindig’s algorithm for concept lattices over graded attributes, Lecture Notes in Computer Science, 4617, 156-167, (2007) · Zbl 1181.68269 [9] Bělohlávek, R.; De Baets, B.; Outrata, J.; Vychodil, V., Computing the lattice of all fixpoints of a fuzzy closure operator, IEEE Transactions on Fuzzy Systems, 18, 3, 546-557, (2010) [10] Benado, M., LES ensembles partiellement ordonnés et le théorème de raffinement de Schreier, II. théorie des multistructures, Czechoslovak Mathematical Journal, 5, 80, 308-344, (1955) · Zbl 0068.25902 [11] Cordero, P.; Gutiérrez, G.; Martínez, J.; de Guzmán, I. P., A new algebraic tool for automatic theorem provers, Annals of Mathematics and Artificial Intelligence, 42, 4, 369-398, (2004) · Zbl 1095.68110 [12] Cornejo, M.; Medina, J.; Ramírez, E., A comparative study of adjoint triples, Fuzzy Sets and Systems, 211, 1-14, (2012) · Zbl 1272.03111 [13] De Baets, B., Analytical solution methods for fuzzy relation equations, (Dubois, D.; Prade, H., The Handbooks of Fuzzy Sets Series, vol. 1, (1999), Kluwer Dordrecht), 291-340 · Zbl 0970.03044 [14] Di Nola, A.; Sanchez, E.; Pedrycz, W.; Sessa, S., Fuzzy relation equations and their applications to knowledge engineering, (1989), Kluwer Academic Publishers Norwell, MA, USA · Zbl 0694.94025 [15] J.C. Díaz, J. Medina, Concept lattices in fuzzy relation equations, in: The 8th International Conference on Concept Lattices and Their Applications, 2011, pp. 75-86. [16] Díaz, J. C.; Medina, J., Solving systems of fuzzy relation equations by fuzzy property-oriented concepts, Information Sciences, 222, 405-412, (2013) · Zbl 1293.68258 [17] I. Düntsch, G. Gediga, Approximation operators in qualitative data analysis, in: Theory and Applications of Relational Structures as Knowledge Instruments, 2003, pp. 214-230. · Zbl 1203.68193 [18] G. Gediga, I. Düntsch, Modal-style operators in qualitative data analysis, in: Proc. IEEE Int. Conf. on Data Mining, 2002, pp. 155-162. [19] Georgescu, G.; Popescu, A., Non-dual fuzzy connections, Archive for Mathematical Logic, 43, 8, 1009-1039, (2004) · Zbl 1060.03042 [20] Lai, H.; Zhang, D., Concept lattices of fuzzy contexts: formal concept analysis vs. rough set theory, International Journal of Approximate Reasoning, 50, 5, 695-707, (2009) · Zbl 1191.68658 [21] Lin, J.-L.; Wu, Y.-K.; Guu, S.-M., On fuzzy relational equations and the covering problem, Information Sciences, 181, 14, 2951-2963, (2011) · Zbl 1231.03047 [22] C. Lindig, Fast concept analysis, in: G. Stumme (Ed.), Working with Conceptual Structures-Contributions to ICCS 2000, 2000, pp. 152-161. [23] Medina, J., Towards multi-adjoint property-oriented concept lattices, Lecture Notes in Artificial Intelligence, 6401, 159-166, (2010) [24] Medina, J., Multi-adjoint property-oriented and object-oriented concept lattices, Information Sciences, 190, 95-106, (2012) · Zbl 1248.68479 [25] Medina, J.; Ojeda-Aciego, M., Multi-adjoint t-concept lattices, Information Sciences, 180, 5, 712-725, (2010) · Zbl 1187.68587 [26] Medina, J.; Ojeda-Aciego, M.; Ruiz-Calviño, J., Fuzzy logic programming via multilattices, Fuzzy Sets and Systems, 158, 674-688, (2007) · Zbl 1111.68016 [27] Medina, J.; Ojeda-Aciego, M.; Ruiz-Calviño, J., Formal concept analysis via multi-adjoint concept lattices, Fuzzy Sets and Systems, 160, 2, 130-144, (2009) · Zbl 1187.68589 [28] Medina, J.; Ojeda-Aciego, M.; Valverde, A.; Vojtáš, P., Towards biresiduated multi-adjoint logic programming, Lecture Notes in Artificial Intelligence, 3040, 608-617, (2004) [29] J. Medina, M. Ojeda-Aciego, P. Vojtáš, Multi-adjoint logic programming with continuous semantics, in: Logic Programming and Non-Monotonic Reasoning, LPNMR’01, Lecture Notes in Artificial Intelligence 2173, 2001, pp. 351-364. · Zbl 1007.68023 [30] J. Medina, J. Ruiz-Calviño, Fuzzy formal concept analysis via multilattices: first prospects and results, in: The 9th International Conference on Concept Lattices and Their Applications (CLA 2012), 2012, pp. 69-79. [31] Pawlak, Z., Rough sets, International Journal of Computer and Information Science, 11, 341-356, (1982) · Zbl 0501.68053 [32] Peeva, K., Resolution of fuzzy relational equations: method, algorithm and software with applications, Information Sciences, 234, 0, 44-63, (2013) · Zbl 1284.03249 [33] Perfilieva, I., Fuzzy function as an approximate solution to a system of fuzzy relation equations, Fuzzy Sets and Systems, 147, 3, 363-383, (2004) · Zbl 1050.03036 [34] Perfilieva, I., Fuzzy relation equations in semilinear spaces, Communications in Computer and Information Science, 80, 545-552, (2010) · Zbl 1207.03066 [35] Perfilieva, I.; Nosková, L., System of fuzzy relation equations with inf-→-composition: complete set of solutions, Fuzzy Sets and Systems, 159, 17, 2256-2271, (2008) · Zbl 1183.03053 [36] Radzikowska, A. M.; Kerre, E. E., A comparative study of fuzzy rough sets, Fuzzy Sets and Systems, 126, 2, 137-155, (2002) · Zbl 1004.03043 [37] Sanchez, E., Resolution of composite fuzzy relation equations, Information and Control, 30, 1, 38-48, (1976) · Zbl 0326.02048 [38] Shieh, B.-S., Solution to the covering problem, Information Sciences, 222, 0, 626-633, (2013) · Zbl 1293.90063 [39] Zadeh, L. A., The concept of a linguistic variable and its application to approximate reasoning I, II, III, Information Sciences, 8-9, 199-257, (1975), 301-357, 43-80 · Zbl 0397.68071 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.