2017-11-22T17:50:36Z
http://ijfs.usb.ac.ir/?_action=export&rf=summon&issue=98
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2004
1
2
Cover Vol.1, No.2
2004
10
29
0
http://ijfs.usb.ac.ir/article_3127_7558552a3bca8b71e43a23a9e773e760.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2004
1
2
A NEW FUZZY MORPHOLOGY APPROACH BASED ON THE FUZZY-VALUED GENERALIZED DEMPSTER-SHAFER THEORY
SAFAR
HATAMI
BABAK N.
ARAABI
CARO
LUCAS
In this paper, a new Fuzzy Morphology (FM) based on the GeneralizedDempster-Shafer Theory (GDST) is proposed. At first, in order to clarify the similarity ofdefinitions between Mathematical Morphology (MM) and Dempster-Shafer Theory (DST),dilation and erosion morphological operations are studied from a different viewpoint. Then,based on this similarity, a FM based on the GDST is proposed. Unlike previous FM’s,proposed FM does not need any threshold to obtain final eroded or dilated set/image. Thedilation and erosion operations are carried out independently but complementarily. The GDSTbased FM results in various eroded and dilated images in consecutive α-cuts, making a nestedset of convex images, where each dilated image at a larger α-cut is a subset of the dilatedimage at a smaller α-cut. Dual statement applies to eroded images.
Generalized Dempster-Shafer theory
Mathematical Morphology
Fuzzy Morphology
Generalized Dempster-Shafer Theory’s Fuzzy Morphology
2004
10
22
1
14
http://ijfs.usb.ac.ir/article_497_1bac70711c4eaabff92384bf9ad33486.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2004
1
2
FUZZY GRADE OF I.P.S. HYPERGROUPS OF ORDER 7
Piergiulio
Corsini
Irina
Cristea
i.p.s. hypergroups are canonical hypergroups such that$[forall(a,x),a+xni x]Longrightarrow[a+x=x].$i.p.s. hypergroups were investigated in [1], [2], [3], [4] and it was proved thatif the order is less than 9, they are strongly canonical (see [13]). In this paperwe obtain the sequences of fuzzy sets and of join spaces determined (see [8])by all i.p.s. hypergroups of order seven. For the meaning of the hypergroupsiH and the notations, see [7], [8].
Fuzzy grade
Strong fuzzy grade
i.p.s. hypergroups
Join spaces
Whypergroups
2004
10
22
15
32
http://ijfs.usb.ac.ir/article_499_f14b97072c6b8a952f174eaabb80457c.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2004
1
2
SOME QUOTIENTS ON A BCK-ALGEBRA GENERATED BY A
FUZZY SET
Abbas
Hasankhani
Hamid
Saadat
First we show that the cosets of a fuzzy ideal μ in a BCK-algebraX form another BCK-algebra X/μ (called the fuzzy quotient BCK-algebra of X by μ). Also we show thatX/μ is a fuzzy partition of X and we prove several some isomorphism theorems. Moreover we prove that if the associated fuzzy similarity relation of a fuzzy partition P of a commutative BCK-algebra iscompatible, then P is a fuzzy quotient BCK-algebra. Finally we define thenotion of a coset of a fuzzy ideal and an element of a BCK-algebra and proverelated theorems.
Fuzzy similarity relations
Fuzzy partitions
Fuzzy quotient
Fuzzy
ideal
Cosets
Quotient algebra
2004
10
22
33
43
http://ijfs.usb.ac.ir/article_503_c99fc7423f434249f96ead64e115875f.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2004
1
2
PEDOMODELS FITTING WITH FUZZY LEAST
SQUARES REGRESSION
JAHANGARD
MOHAMMADI
SYED MAHMOUD
TAHERI
Pedomodels have become a popular topic in soil science and environmentalresearch. They are predictive functions of certain soil properties based on other easily orcheaply measured properties. The common method for fitting pedomodels is to use classicalregression analysis, based on the assumptions of data crispness and deterministic relationsamong variables. In modeling natural systems such as soil system, in which the aboveassumptions are not held true, prediction is influential and we must therefore attempt toanalyze the behavior and structure of such systems more realistically. In this paper weconsider fuzzy least squares regression as a means of fitting pedomodels. The theoretical andpractical considerations are illustrated by developing some examples of real pedomodels.
Pedomodels
Pedotransfer Functions
Fuzzy Least Squares
Fuzzy regression
2004
10
22
45
61
http://ijfs.usb.ac.ir/article_505_dcb76238bd5f980beec986293a3c294e.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2004
1
2
FUZZY (POSITIVE, WEAK) IMPLICATIVE HYPER
BCK-IDEALS
Mahmood
Bakhshi
Rajab Ali
Borzooei
Mohammad Mehdi
Zahedi
In this note first we define the notions of fuzzy positive implicativehyper BCK-ideals of types 1,2,3 and 4. Then we prove some theorems whichcharacterize the above notions according to the level subsets. Also we obtainthe relationships among these notions, fuzzy (strong, weak, reflexive) hyperBCK-ideals and fuzzy positive implicative hyper BCK-ideals of types 5,6,7and 8. Then, we define the notions of fuzzy (weak) implicative hyper BCKidealsand we obtain some related results. Finally, by considering the productof two hyper BCK-algebras we give some theorems which show that how theprojections of a fuzzy (positive implicative, implicative) hyper BCK-ideal isagain a fuzzy (positive implicative, implicative) hyper BCK-ideal.
Hyper BCK-algebra
Fuzzy (strong
weak
reflexive) hyper BCKideal
Fuzzy (positive
weak) implicative hyper BCK-ideals
2004
10
22
63
79
http://ijfs.usb.ac.ir/article_506_1cd574ace5a8a66b1d6f21e939dc2ec1.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2004
1
2
Persian-translation vol.1, no.2
2004
10
29
82
90
http://ijfs.usb.ac.ir/article_3128_57612763133852cb23303b08bb744903.pdf