نتایج جستجو برای: 2 fuzzy

تعداد نتایج: 2607514  

2017
A. Nagoor Gani Prasanna Devi

In this paper, 2−bondage set of a fuzzy graph G is defined. The 2−bondage number, b2(G) is the minimum cardinality among all 2−bondage sets of G. The condition for a 2−bondage set of a fuzzy graph to be a bondage set is also given. The exact values of b2(G) is determined for several classes of fuzzy graphs.

Journal: :J. Intelligent Manufacturing 2012
Paraskevi Th. Zacharia Andreas C. Nearchou

This paper presents a fuzzy extension of the simple assembly line balancing problem of type 2 (SALBP-2) with fuzzy job processing times since uncertainty, variability, and imprecision are often occurred in real-world production systems. The jobs processing times are formulated by triangular fuzzy membership functions. The total fuzzy cost function is formulated as the weighted-sum of two bi-cri...

Journal: :Expert Syst. Appl. 2015
Lazim Abdullah Norsyahida Zulkifli

The fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy decision making trial and evaluation laboratory (fuzzy DEMATEL) have been used to obtain weights for criteria and relationships among dimensions and criteria respectively. The twomethods could be integrated since it serves different purposes. Previous research suggested that the weights of criteria and the relationships among dimensions...

2012
Luciano Stefanini Laerte Sorini

Abstract The fuzzy transform setting (F-transform) is proposed as a tool for representation and approximation of type-1 and type-2 fuzzy numbers; the inverse F-transform on appropriate fuzzy partition of the membership interval [0,1] is used to characterize spaces of fuzzy numbers in such a way that arithmetic operations are de…ned and expressed in terms of the F-transform of the results. A typ...

2009
Gerhard Preuss

Using fuzzy filters in the sense of P. Eklund and W. Gähler [2], it turns out that fuzzy preuniform convergence spaces introduced in [11] form a strong topological universe in which fuzzy topological spaces as well as fuzzy (quasi) uniform spaces can be studied. Thus, better tools such as the existence of natural function spaces, the existence of one-point extensions (and consequently, the here...

Journal: :IEEE Trans. Fuzzy Systems 2001
Qilian Liang Jerry M. Mendel

In this paper, we present a new approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation ...

2014
Sun Sook Jin Yang-Hi Lee

and Applied Analysis 3 is Cauchy. If each Cauchy sequence is convergent, then the fuzzy norm is said to be complete, and the fuzzy normed space is called a fuzzy Banach space. Let X,N be a fuzzy normed space and Y,N ′ a fuzzy Banach space. For a given mapping f : X → Y , we use the abbreviation Df ( x, y ) : f ( 2x y ) f ( 2x − y 2f x − fx y − fx − y − 2f 2x , 2.1 for all x, y ∈ X. Recall Df ≡ ...

2011
Jörg Verstraete

Spatial data is quite often is prone to uncertainty and imprecision. For this purpose, fuzzy regions have been developed: they basically consist of a fuzzy set over a two dimensional domain, allowing for both fuzzy regions and fuzzy points to be modelled. The model is extended to a level-2 fuzzy region to overcome some limitations, but this has an impact on operations. In this contribution, we ...

2005
A. P. Shostak

Introduction § 0. Preliminaries: fuzzy sets 125 § 1. Fuzzy topological spaces: the basic categories of fuzzy topology 127 § 2. Fundamental interrelations between the category Top of topological 135 spaces and the categories of fuzzy topology § 3. Local structure of fuzzy topological spaces 138 § 4. Convergence structures in fuzzy spaces 140 § 5. Separation in fuzzy spaces 143 § 6. Normality and...

2001
KWEIMEI WU

For any two points P = (p (1) ,p (2) ,...,p (n)) and Q = (q (1) ,q (2) ,...,q (n)) of R n , we define the crisp vector → PQ = (q (1) −p (1) ,q (2) −p (2) ,...,q (n) −p (n)) = Q(−)P. Then we obtain an n-dimensional vector space E n = { → PQ | for all P,Q ∈ R n }. Further, we extend the crisp vector into the fuzzy vector on fuzzy sets of R n. Let D, E be any two fuzzy sets on R n and define the f...

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