A Study on Fuzzy Relational Mapping (FRM)
DOI:
https://doi.org/10.26438/ijcse/v5i8.106109Keywords:
FRM, Fuzzy Logic, Binary Algorithm, Fuzzy OptimizationAbstract
The fuzzy model is a limited arrangement of fuzzy relations that frame a calculation for deciding the yields of a procedure from some limited number of past data sources and yields. Fuzzy model can be utilized as a part of connected mathematics, to contemplate social and mental issue and furthermore utilized by specialists, design, researchers, industrialists and analysts. There are different sorts of fuzzy models. In this paper we utilize two fuzzy models and give their application to a genuine issue. In this paper two methodologies of fuzzy capacity have been researched: the first distinguishes a fuzzy capacity with a special fuzzy relation (we call it an (E − F)- fuzzy capacity), and the second one characterizes a fuzzy capacity as a conventional mapping between fuzzy spaces. In our exchange the components of the area space are taken from the genuine vector space of measurement n and that of the range space are genuine vectors from the vector space of measurement (m when all is said in done need not be equivalent to n). We mean by R the arrangement of hubs R1, … , Rm of the range space, where Ri = {(x1, x2, … , xm)/xj = 0 or 1} for i = 1, … ,m. In the event that xi = 1 it implies that the hub Ri is in the ON state and if xi = 0 it implies that the hub Ri is in the OFF state.
References
M. Baaz: Infinite-valued Godel¨ logics with 0-1 projections and relativizations. GODEL¨ ’96 – Logical Foundations of Mathematics, Computer Sciences and Physics, Lecture Notes in Logic vol. 6, Springer-Verlag 1996, 23–33.
M. Abinaya, V. Ramadass, "A Study on Fuzzy Relational Mapping (FRM)", International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.109-113, 2017.
W. Bandler, L. J. Kohout, “Semantics of implication operators and fuzzy relational products”, International Journal of Man-Machine Studies, 12, 1980, 89–116.
S. Hemba, N. Islam , "Fuzzy Logic: A Review", International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.61-63, 2017.
E. Bartl, R. Belohlavek, “Reducing sup-t-norm and inf-residuum to a single type of fuzzy relational equations”, In: NAFIPS 2011, Fuzzy Information Processing Society, El Paso, TX, USA, March 18-20, 2011, pp. 1-5.
E. Bartl, R. Belohlavek, “Sup-t-norm and inf-residuum are a single type of relational equations”, International Journal of General Systems 40 (6)(2011), pp. 599-609.
A. Yadav, V.K. Harit, "Fault Identification in Sub-Station by Using Neuro-Fuzzy Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.6, pp.1-7, 2016.
E. Bartl, R. Belohlavek, V. Vychodil, “Compositions of fuzzy relations with hedges II”, In: NAFIPS 2008, The Fourth International IEEE Conference on Intelligent Systems, New York City, NY, USA, May 19-22, 2008, pp. 1-6.
E. Bartl, R. Belohlavek, V. Vychodil, “Bivalent and Other Solutions of Fuzzy Relational Equations via Linguistic Hedges”, Fuzzy Sets and Systems 181(1)(2012), pp. 103-112.
R. Belohlavek, “Fuzzy Relational Systems: Foundations and Principles”. Kluwer Academic/Plenum Publishers, New York, 2002.
R. Belohlavek, “Sup-t-norm and inf-residuum are one type of relational product: unifying framework and consequences”, Fuzzy Sets and Systems, Volume 197, June, 2012, pp. 45-58.
R. Belohlavek, T. Funiokova,´ V. Vychodil, “Fuzzy closure operators with truth stressers”, Logic J. of IGPL 13(5)(2005), pp. 503–513.
R. Belohlavek, V. Vychodil, “What is a fuzzy concept lattice?”, In: Proc. CLA 2005, 3rd Int. Conference on Concept Lattices and Their Applications, 2005, pp. 34–45.
R. Belohlavek, V. Vychodil, “Reducing the size of fuzzy concept lattices by hedges”, Proc. FUZZ-IEEE, pp. 663–668, Reno, Nevada, 2005. In: FUZZIEEE 2005, The IEEE International Conference on Fuzzy Systems, May 22– 25, 2005, Reno (Nevada, USA), pp. 663–668 (proceedings on CD), abstract in printed proceedings, pp. 44.
R. Belohlavek, V. Vychodil, “Attribute implications in a fuzzy setting”, In: Missaoui R., Schmid J. (Eds.): Proc. ICFCA 2006, LNAI 3874, 2006, pp. 45–60.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
