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Novel approximate solving algorithm for fuzzy relational equations. (English) Zbl 1201.90209

Math. Comput. Modelling 52, No. 1-2, 303-308 (2010); corrigendum 54, No. 11-12, 3211 (2011).
Summary: This paper presents a novel approximate solution algorithm for fuzzy relational equations with max-product composition. Solving fuzzy relational equations is a very important research topic because many practical engineering problems end up with fuzzy relational equations (F.R.E). Most theoretical results on F.R.E. strongly rely on an assumption that the family of exact solutions is nonempty. However, the fuzzy relational equations may no solutions. Therefore, this paper proposes real-valued GA method to find an approximate solution for fuzzy relational equations with max-product composition. An example illustrates that the proposed algorithm is effective and simple.

MSC:

90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
15B15 Fuzzy matrices
Full Text: DOI

References:

[1] Sanchez, E., Resolution of composite fuzzy relation equations, Information and Control, 30, 38-48 (1976) · Zbl 0326.02048
[2] Higashi, M.; Klir, G. J., Resolution of finite fuzzy relation equations, Fuzzy Sets and Systems, 13, 65-82 (1984) · Zbl 0553.04006
[3] Wang, H.-F.; Chang, Y.-C., Resolution of composite interval-valued fuzzy relation equations, Fuzzy Sets and Systems, 44, 227-240 (1991) · Zbl 0738.04003
[4] Sessa, S., Some results in the setting fuzzy relation equations theory, Fuzzy Sets and Systems, 14, 281-297 (1984) · Zbl 0559.04005
[5] Sessa, S., Finite fuzzy relation equations with unique solution in complete brouwerian lattices, Fuzzy Sets and Systems, 29, 103-113 (1989) · Zbl 0659.06007
[6] Lettieri, A.; Liguori, F., Characterization of some fuzzy relation equations provided with one solution on a finite set, Fuzzy Sets and Systems, 13, 83-94 (1984) · Zbl 0553.04004
[7] Baets, B. D., Analytical solution methods for fuzzy relational equations, (Dubois, D.; Prade, H., Fundamentals of Fuzzy Sets. Fundamentals of Fuzzy Sets, The Handbooks of Fuzzy Sets Series, vol. 1 (2000), Kluwer: Kluwer Norwell, MA), 291-340 · Zbl 0970.03044
[8] Klir, G. J.; Yaun, B., Fuzzy Sets and Fuzzy Logic: Theory and Applications (1995), Prentice-Hall: Prentice-Hall Upper Saddle River, NJ · Zbl 0915.03001
[9] Khorram, Esmaile; Ghodousian, Amin; Molai, Ali Abbasi, Solving linear optimization problems with max-star composition equation constraints, Applied Mathematics and Computation, 179, 2, 654-661 (2006) · Zbl 1103.65067
[10] Khorram, E.; Zarei, H., Multi-objective optimization problems with fuzzy relation equation constraints regarding max-average composition, Mathematical and Computer Modelling, 49, 5-6, 856-867 (2009) · Zbl 1165.90626
[11] Kuo, Ming-Shin; Tzeng, Gwo-Hshiung; Huang, Wen-Chih, Group decision-making based on concepts of ideal and anti-ideal points in a fuzzy environment, Mathematical and Computer Modelling, 45, 3-4, 324-339 (2007) · Zbl 1170.90421
[12] Nola, A. D.; Sessa, S.; Pedrycz, W.; Sanchez, E., Fuzzy Relation Equations and their Applications to Knowledge Engineering (1989), Kluwer: Kluwer Norwell, MA · Zbl 0694.94025
[13] Pedrycz, W.; Gomide, F., An Introduction to Fuzzy Sets: Analysis and Design (1998), MIT Press: MIT Press Cambridge, MA · Zbl 0938.03078
[14] Wang, Y. M.; Fan, Z. P., Group decision analysis based on fuzzy preference relations: logarithmic and geometric least squares methods, Applied Mathematics and Computation, 194, 1, 108-119 (2007) · Zbl 1193.91045
[15] Wu, W., Fuzzy reasoning and fuzzy relational equations, Fuzzy Sets and Systems, 20, 1, 67-78 (1986) · Zbl 0629.94031
[16] Wu, Yan-Kuen, Optimizing the geometric programming problem with single-term exponents subject to max-min fuzzy relational equation constraints, Mathematical and Computer Modelling, 47, 3-4, 352-362 (2008) · Zbl 1171.90572
[17] Adamopoulos, G. I.; Pappis, C. P., Some results on the resolution of fuzzy relation equations, Fuzzy Sets and Systems, 60, 83-88 (1993) · Zbl 0794.04005
[18] Czogala, E.; Drewniak, J.; Pedrycz, W., Fuzzy relation equations on a finite set, Fuzzy Sets and Systems, 7, 89-101 (1982) · Zbl 0483.04001
[19] Li, X.; Ruan, D., Novel neural algorithms based on fuzzy \(\delta\) rules for solving fuzzy relation equations: part III, Fuzzy Sets and Systems, 109, 355-362 (2000) · Zbl 0956.68131
[20] Li, Pingke; Fang, Shu-Cherng, A note on solution sets of interval-valued fuzzy relational equations, Fuzzy Optimization and Decision Making, 8, 1, 115-121 (2009) · Zbl 1179.03052
[21] Perfilieva, Irina; Novák, Vilém, System of fuzzy relation equations as continuous model of IF-THEN rules, Information Sciences, 177, 3218-3227 (2007) · Zbl 1124.03029
[22] Shieh, B. S., Solutions of fuzzy relation equations based on continuous \(t\)-norms, Information Sciences, 177, 4208-4215 (2007) · Zbl 1122.03054
[23] Shieh, Bih-Sheue, Driving minimal solutions for fuzzy relation equations with max-product composition, Information Sciences, 178, 19, 3766-3774 (2008) · Zbl 1151.03345
[24] Wang, H.-F.; Hsu, H.-M., An alternative approach to the resolution of fuzzy relation equations, Fuzzy Sets and Systems, 45, 203-213 (1992) · Zbl 0761.04006
[25] L.-X. Wang, Solving fuzzy relational equations through network training, in: Proc. 2nd IEEE Int. Conf., 1993, pp. 956-960.; L.-X. Wang, Solving fuzzy relational equations through network training, in: Proc. 2nd IEEE Int. Conf., 1993, pp. 956-960.
[26] Yeh, Chi-Tsuen, On the minimal solutions of max-min fuzzy relational equations, Fuzzy Sets and Systems, 159, 1, 23-39 (2008) · Zbl 1176.03040
[27] Li, Pingke; Fang, Shu-Cherng, A survey on fuzzy relational equations, part I: classification and solvability, Fuzzy Optimization and Decision Making, 8, 2, 179-229 (2009) · Zbl 1180.03051
[28] Molai, Ali Abbasi; Khorram, Esmaile, An algorithm for solving fuzzy relation equations with max-\(T\) composition operator, Information Sciences, 178, 1293-1308 (2008) · Zbl 1136.03330
[29] Pappis, C. P.; Sugeno, M., Fuzzy relational equations and the inverse problem, Fuzzy Sets and Systems, 15, 79-90 (1985) · Zbl 0561.04003
[30] Pappis, C. P., Resolution of Cartesian products of fuzzy sets, Fuzzy Sets and Systems, 26, 387-391 (1988) · Zbl 0664.04005
[31] Pappis, C. P.; Adamopoulos, G. I., A computer algorithm for the solution of the inverse problem of fuzzy systems, Fuzzy Sets and Systems, 39, 279-290 (1991) · Zbl 0727.93029
[32] Pappis, C. P.; Adamopoulos, G. I., A software routine to solve the genearalized inverse problem of fuzzy relational equations, Fuzzy Sets and Systems, 47, 319-322 (1992) · Zbl 0850.93447
[33] Luoh, Leh; Wang, W. J.; Liaw, Yi-Ke, New algorithms for solving fuzzy relation equations, Mathematics and Computers in Simulation, 59, 4, 329-333 (2002) · Zbl 0999.03513
[34] Luoh, Leh; Wang, W. J.; Liaw, Yi-Ke, Matrix-pattern-based computer algorithm for solving fuzzy relation equations, IEEE Transactions on Fuzzy Systems, 11, 2, 100-110 (2003)
[35] Imai, Hideyuki; Kikuchi, Ken; Miyakoshi, Masaaki, Unattainable solutions of a fuzzy relation equation, Fuzzy Sets and Systems, 99, 193-196 (1998) · Zbl 0938.03081
[36] Pedrycz, W., Numerical and applicational aspects of fuzzy relational equations, Fuzzy Sets and Systems, 11, 1-18 (1983) · Zbl 0517.93001
[37] Gottwald, S.; Pedrycz, W., Solvability of fuzzy relational equations and manipulation of fuzzy data, Fuzzy Sets and Systems, 18, 1, 45-65 (1986) · Zbl 0607.94015
[38] G.J. Klir, Bo Yuan, Approximate solutions of systems of fuzzy relation equations, in: Conference on IEEE, 1994, pp. 1452-1457.; G.J. Klir, Bo Yuan, Approximate solutions of systems of fuzzy relation equations, in: Conference on IEEE, 1994, pp. 1452-1457.
[39] Lee, Loo H., An adaptive real-coded genetic algorithm, Applied Artificial Intelligence, 16, 6, 457-486 (2002)
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