scholarly journals Residual quotient fuzzy subset in near-rings

Author(s):  
P. Dheena ◽  
S. Coumaressane
Keyword(s):  

For any fuzzy subsetsλandμ, we introduce the notion of residual quotient fuzzy subset (λ:μ) and we have characterized residual quotient fuzzy subset in near-rings.

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Seung Joon Shin ◽  
Hee Sik Kim ◽  
J. Neggers

We discuss properties of a class of real-valued functions on a setX2constructed as finite (real) linear combinations of functions denoted asX,*;μ, whereX,*is a groupoid (binary system) andμis a fuzzy subset ofXand whereX,*;μx,y≔μx*y-minμx,μy. Many properties, for example,μbeing a fuzzy subgroupoid ofX,*, can be restated as some properties ofX,*;μ. Thus, the context provided opens up ways to consider well-known concepts in a new light, with new ways to prove known results as well as to provide new questions and new results. Among these are identifications of many subsemigroups and left ideals ofBinX;□for example.


2017 ◽  
Vol 26 (1) ◽  
pp. 46-56
Author(s):  
Niovi Kehayopulu

Abstract We prove that an hypersemigroup H is regular if and only, for any fuzzy subset f of H, we have f ≼ f ο 1 ο f and it is intra-regular if and only if, for any fuzzy subset f of H, we have f ≼1 ο f ο f ο 1. An hypersemigroup H is left (resp. right) quasi-regular if and only if, for any fuzzy subset f of H we have f ≼ 1 ο f ο 1 ο f (resp. f ≼ f ο 1 ο f ο 1) and it is semisimple if and only if, for any fuzzy subset f of H we have f ≼ 1 ο f ο 1 ο f ο 1. The characterization of regular and intra-regular hypersemigroups in terms of fuzzy subsets are very useful for applications.


1980 ◽  
Vol 19 (03) ◽  
pp. 141-148 ◽  
Author(s):  
K.-P. Adlassnig

A model of a computer-assisted diagnostic system using fuzzy subsets has been developed. The physician documents symptom—diagnosis presence relationships and symptom—diagnosis conclusiveness relationships by means of labels of the fuzzy subsets never, almost never, very very seldom, very seldom, seldom, more or less seldom, not known, more or less often, often, very often, very very often, almost always, always. Symptoms are regarded as fuzzy subsets of reference sets. The reference set includes all values the symptom may assume. The degree of membership of a value in the fuzzy subset of a symptom is calculated when the patient’s symptom pattern is available. By means of compositions of fuzzy relations, four different diagnostic indications are determined for every diagnosis under consideration: presence indication, conclusiveness indication, non-presence indication and non-symptom presence indication. By performing the diagnostic process, the system provides the physician with proven diagnoses, excluded diagnoses and diagnostic hints, including reasons for the diagnoses displayed. Proposals for further investigations may also be requested.


1980 ◽  
Vol 19 (03) ◽  
pp. 141-148
Author(s):  
K.-P. Adlassnig

A model of a computer-assisted diagnostic system using fuzzy subsets has been developed. The physician documents symptom—diagnosis presence relationships and symptom—diagnosis conclusiveness relationships by means of labels of the fuzzy subsets never, almost never, very very seldom, very seldom, seldom, more or less seldom, not known, more or less often, often, very often, very very often, almost always, always. Symptoms are regarded as fuzzy subsets of reference sets. The reference set includes all values the symptom may assume. The degree of membership of a value in the fuzzy subset of a symptom is calculated when the patient’s symptom pattern is available. By means of compositions of fuzzy relations, four different diagnostic indications are determined for every diagnosis under consideration: presence indication, conclusiveness indication, non-presence indication and non-symptom presence indication. By performing the diagnostic process, the system provides the physician with proven diagnoses, excluded diagnoses and diagnostic hints, including reasons for the diagnoses displayed. Proposals for further investigations may also be requested.


Author(s):  
J. Recasens ◽  
J. Lawry

A new approach to normalizing fuzzy sets is introduced where it is assumed that the normalization method is compatible with a given t-norm. In this context it is proved that the most usual ways to normalize fuzzy subsets correspond to the most common t-norms. For a given fuzzy subset μ, the corresponding normalized fuzzy subset [Formula: see text] can be viewed as the distribution of μ conditioned on the (degree of) existence of its elements with maximal membership. From this view point we investigate the less specific normal fuzzy subset of X among the most similar fuzzy subsets to μ and the normal fuzzy subset generating the same fuzzy T-preorder as μ.


2018 ◽  
Vol 7 (3.27) ◽  
pp. 513
Author(s):  
Suad Abdulaali Neamah ◽  
. .

This  work  involved  some  studying ,we study and prove some theorems and a propositions which determine the relationships among the notion of  fuzzy p-ideal with the intersection, union, image of function, inverse function  and with some other fuzzy subsets of BH-algebra, also we gave some properties of this ideal of a BH-algebra.  


2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Xiang Zhu ◽  
Md Gapar Md Johar ◽  
Lilysuriazna Binti Raya ◽  
Zu-hua Liao

The new concept of the fuzzy filter degree was given by means of the implication operator, which enables to measure a degree to which a fuzzy subset of a BL-algebra is a fuzzy filter. In this paper, we put forward several equivalent characterizations of the fuzzy filter degree by studying its properties and the relationship with level cut sets. Furthermore, we study the fuzzy filter degrees of the intersection and fuzzy direct products of fuzzy subsets and investigate the fuzzy filter degrees of the image and the preimage of a fuzzy subset under a homomorphism.


2014 ◽  
Vol 6 (1) ◽  
pp. 33
Author(s):  
Fatkhur Rozi ◽  
Ari Wardayani ◽  
Suroto Suroto

This paper discusses a fuzzy subgroup of a classical group. It’s constructed by defining fuzzy subsets and employing products and inverse notions on classical group. The result obtained is sufficient and necessary conditions for the fuzzy subset to be fuzzy subgroup.


1978 ◽  
Vol 17 (01) ◽  
pp. 1-10 ◽  
Author(s):  
P. Tautu ◽  
G. Wagner

This paper is an analysis of the most important mathematical aspects of medical diagnosis: logical probability, rationality and decision theory, gambling models, pattern analysis, hazy and fuzzy subsets theory and, finally, the stochastic inquiry process.


1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.


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