International Journal of
Computer Sciences and Engineering

Scholarly Peer-Reviewed, and Fully Refereed Scientific Research Journal
A Study on Fuzzy Relational Mapping (FRM)
A Study on Fuzzy Relational Mapping (FRM)
M. Abinaya1 , V. Ramadass2
1 Department of Mathematics, Prist University, Thanjavur, Tamilnadu.
2 Department of Mathematics, Prist University, Thanjavur, Tamilnadu.
Correspondence should be addressed to: abinayamsc1993@gmail.com.

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-8 , Page no. 106-109, Aug-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i8.106109

Online published on Aug 30, 2017

Copyright © M. Abinaya, V. Ramadass . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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IEEE Style Citation: M. Abinaya, V. Ramadass, “A Study on Fuzzy Relational Mapping (FRM)”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.106-109, 2017.

MLA Style Citation: M. Abinaya, V. Ramadass "A Study on Fuzzy Relational Mapping (FRM)." International Journal of Computer Sciences and Engineering 5.8 (2017): 106-109.

APA Style Citation: M. Abinaya, V. Ramadass, (2017). A Study on Fuzzy Relational Mapping (FRM). International Journal of Computer Sciences and Engineering, 5(8), 106-109.
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Abstract :
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. Additionally D signifies the hubs D1,… ,Dn of the area space where Di = {(x1,… , xn)/xj = 0 or 1} for I = 1, … , n. In the event that xi = 1, it implies that the hub Di is in the on state and if xi = 0 it implies that the hub Di is in the off state. A FRM is a directed graph or a guide from D to R with ideas like arrangements or occasions and so forth as hubs and causalities as edges. It speaks to easygoing relations between spaces D and R. Give Di and Rj a chance to signify the two hubs of a FRM.
Key-Words / Index Term :
FRM, Fuzzy Logic, Binary Algorithm, Fuzzy Optimization
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