University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
4
1
2007
04
30
Cover Vol.4, No.1 April 2007
0
EN
10.22111/ijfs.2007.2910
http://ijfs.usb.ac.ir/article_2910.html
http://ijfs.usb.ac.ir/article_2910_2e784435556c4da339b26e553e994ebf.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
4
1
2007
04
09
DISTRIBUTED AND COLLABORATIVE FUZZY MODELING
1
19
EN
WITOLD
PEDRYCZ
DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING, UNIVERSITY OF ALBERTA,
EDMONTON T6R 2G7 CANADA AND SYSTEMS RESEARCH INSTITUTE OF THE POLISH ACADEMY OF SCIENCE,
WARSAW, POLAND
pedrycz@ee.ualberta.ca
10.22111/ijfs.2007.353
In this study, we introduce and study a concept of distributed fuzzymodeling. Fuzzy modeling encountered so far is predominantly of a centralizednature by being focused on the use of a single data set. In contrast to this style ofmodeling, the proposed paradigm of distributed and collaborative modeling isconcerned with distributed models which are constructed in a highly collaborativefashion. In a nutshell, distributed models reconcile and aggregate findings of theindividual fuzzy models produced on a basis of local data sets. The individualmodels are formed in a highly synergistic, collaborative manner. Given the fact thatfuzzy models are inherently granular constructs that dwell upon collections ofinformation granules – fuzzy sets, this observation implies a certain generaldevelopment process. There are two fundamental design issues of this style ofmodeling, namely (a) a formation of information granules carried out on a basis oflocally available data and their collaborative refinement, and (b) construction oflocal models with the use of properly established collaborative linkages. We discussthe underlying general concepts and then elaborate on their detailed development.Information granulation is realized in terms of fuzzy clustering. Local modelsemerge in the form of rule-based systems. The paper elaborates on a number ofmechanisms of collaboration offering two general categories of so-calledhorizontal and vertical clustering. The study also addresses an issue ofcollaboration in cases when such interaction involves information granules formedat different levels of specificity (granularity). It is shown how various algorithms ofcollaboration lead to the emergence of fuzzy models involving informationgranules of higher type such as e.g., type-2 fuzzy sets.
Computational Intelligence,C^{3} paradigm,Distributed processing,Fuzzy
clustering,Fuzzy models
http://ijfs.usb.ac.ir/article_353.html
http://ijfs.usb.ac.ir/article_353_e16a2e92cedec4f2fbb09d7c81a6cb72.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
4
1
2007
04
09
USING DISTRIBUTION OF DATA TO ENHANCE PERFORMANCE OF FUZZY CLASSIFICATION SYSTEMS
21
36
EN
EGHBAL G.
MANSOORI
COMPUTER SCIENCE AND ENGINEERING DEPARTMENT, COLLEGE OF
ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
mansoori@shirazu.ac.ir
MANSOOR J.
ZOLGHADRI
COMPUTER SCIENCE AND ENGINEERING DEPARTMENT, COLLEGE OF
ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
zjahromi@shirazu.ac.ir
SERAJ D.
KATEBI
COMPUTER SCIENCE AND ENGINEERING DEPARTMENT, COLLEGE OF ENGINEERING,
SHIRAZ UNIVERSITY, SHIRAZ, IRAN
katebi@shirazu.ac.ir
10.22111/ijfs.2007.355
This paper considers the automatic design of fuzzy rule-basedclassification systems based on labeled data. The classification performance andinterpretability are of major importance in these systems. In this paper, weutilize the distribution of training patterns in decision subspace of each fuzzyrule to improve its initially assigned certainty grade (i.e. rule weight). Ourapproach uses a punishment algorithm to reduce the decision subspace of a ruleby reducing its weight, such that its performance is enhanced. Obviously, thisreduction will cause the decision subspace of adjacent overlapping rules to beincreased and consequently rewarding these rules. The results of computersimulations on some well-known data sets show the effectiveness of ourapproach.
Fuzzy rule-based classification systems,Rule weight
http://ijfs.usb.ac.ir/article_355.html
http://ijfs.usb.ac.ir/article_355_8012934623da6f85204a71c666c34242.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
4
1
2007
04
09
FUZZY BASED FAULT DETECTION AND CONTROL FOR 6/4 SWITCHED RELUCTANCE MOTOR
37
51
EN
N.
SELVAGANESAN
DEPARTMENT OF ELECTRICAL & ELECTRONICS ENGINEERING, PONDICHERRY
ENGINEERING COLLEGE, PONDICHERRY-605014, INDIA
n_selvag@rediffmail.com
D.
RAJA
DEPARTMENT OF ELECTRICAL & ELECTRONICS ENGINEERING, PONDICHERRY ENGINEERING
COLLEGE, PONDICHERRY-605014, INDIA
S.
SRINIVASAN
DEPARTMENT OF INSTRUMENTATION ENGINEERING, MIT CAMPUS, ANNA UNIVERSITY,
CHROMEPET, CHENNAI-600044, INDIA
srini@mitindia.edu
10.22111/ijfs.2007.356
Prompt detection and diagnosis of faults in industrial systems areessential to minimize the production losses, increase the safety of the operatorand the equipment. Several techniques are available in the literature to achievethese objectives. This paper presents fuzzy based control and fault detection for a6/4 switched reluctance motor. The fuzzy logic control performs like a classicalproportional plus integral control, giving the current reference variation based onspeed error and its change. Also, the fuzzy inference system is created and rulebase are evaluated relating the parameters to the type of the faults. These rules arefired for specific changes in system parameters and the faults are diagnosed. Thefeasibility of fuzzy based fault diagnosis and control scheme is demonstrated byapplying it to a simulated system.
Fault Diagnosis,Fuzzy Logic,Switched Reluctance Motor,Fuzzy
Inference Systems
http://ijfs.usb.ac.ir/article_356.html
http://ijfs.usb.ac.ir/article_356_9057931d5fc7d9338fab8be76e173fb2.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
4
1
2007
04
09
SOME RESULTS ON INTUITIONISTIC FUZZY SPACES
53
64
EN
S. B.
Hosseini
Islamic Azad University-Nour Branch, Nour, Iran
Donal
O’Regan
Department of Mathematics, National University of Ireland, Galway,
Ireland
donal.oregan@nuigalway.ie
Reza
Saadati
Department of Mathematics, Islamic Azad University-Ayatollah Amoly
Branch, Amol, Iran and Institute for Studies in Applied Mathematics 1, Fajr 4, Amol
46176-54553, Iran
rsaadati@eml.cc
10.22111/ijfs.2007.357
In this paper we define intuitionistic fuzzy metric and normedspaces. We first consider finite dimensional intuitionistic fuzzy normed spacesand prove several theorems about completeness, compactness and weak convergencein these spaces. In section 3 we define the intuitionistic fuzzy quotientnorm and study completeness and review some fundamental theorems. Finally,we consider some properties of approximation theory in intuitionistic fuzzymetric spaces.
Intuitionistic fuzzy metric (normed) spaces,Completeness,Compactness,Finite dimensional,Weak convergence,Quotient spaces,Approximation theory
http://ijfs.usb.ac.ir/article_357.html
http://ijfs.usb.ac.ir/article_357_3141e238301eb28b17f345772521dbda.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
4
1
2007
04
09
L-FUZZY BILINEAR OPERATOR AND ITS CONTINUITY
65
73
EN
Cong-hua
Yan
Department of Mathematics, Nanjing Normal University, Nanjing
Jiangsu, 210097, P.R.China
chyan@njnu.edu.cn
Jin-xuan
Fang
Department of Mathematics, Nanjing Normal University, Nanjing
Jiangsu, 210097, P.R.China
jxfang@njnu.edu.cn
10.22111/ijfs.2007.358
The purpose of this paper is to introduce the concept of L-fuzzybilinear operators. We obtain a decomposition theorem for L-fuzzy bilinearoperators and then prove that a L-fuzzy bilinear operator is the same as apowerset operator for the variable-basis introduced by S.E.Rodabaugh (1991).Finally we discuss the continuity of L-fuzzy bilinear operators.
Order-homomorphism,Powerset operator,L-bilinear operator,Molecule
net
http://ijfs.usb.ac.ir/article_358.html
http://ijfs.usb.ac.ir/article_358_af8602c55b74198bfce9a442e63cec84.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
4
1
2007
04
09
TRIANGULAR FUZZY MATRICES
75
87
EN
Amiya Kumar l
Shyama
Department of Applied Mathematics
with Oceanology and Computer Programming, Vidyasagar University, Midnapore -
721102, West Bengal, India
Madhumangal
Pal
Department of Applied Mathematics
with Oceanology and Computer Programming, Vidyasagar University, Midnapore -
721102, West Bengal, India
madhumangal@lycos.com
10.22111/ijfs.2007.359
In this paper, some elementary operations on triangular fuzzynumbers (TFNs) are defined. We also define some operations on triangularfuzzy matrices (TFMs) such as trace and triangular fuzzy determinant(TFD). Using elementary operations, some important properties of TFMs arepresented. The concept of adjoints on TFM is discussed and some of theirproperties are. Some special types of TFMs (e.g. pure and fuzzy triangular,symmetric, pure and fuzzy skew-symmetric, singular, semi-singular, constant)are defined and a number of properties of these TFMs are presented.
Triangular fuzzy numbers,Triangular fuzzy number arithmetic,Triangular
fuzzy matrices,Triangular fuzzy determinant
http://ijfs.usb.ac.ir/article_359.html
http://ijfs.usb.ac.ir/article_359_0038d4fb0f550de224041cbbbd77caf6.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
4
1
2007
04
09
INTUITIONISTIC FUZZY BOUNDED LINEAR OPERATORS
89
101
EN
S.
Vijayabalaji
Department of Mathematics, Annamalai University, Annamalainagar-
608002, Tamilnadu, India
balaji−nandini@rediffmail.com
N.
Thillaigovindan
Department of Mathematics Section, Faculty of Engineering and
Technology, Annamalai University, Annamalainagar-608002, Tamilnadu, India
thillai−n@sify.com
10.22111/ijfs.2007.361
The object of this paper is to introduce the notion of intuitionisticfuzzy continuous mappings and intuitionistic fuzzy bounded linear operatorsfrom one intuitionistic fuzzy n-normed linear space to another. Relation betweenintuitionistic fuzzy continuity and intuitionistic fuzzy bounded linearoperators are studied and some interesting results are obtained.
fuzzy n-norm,intuitionistic fuzzy n-norm,intuitionistic fuzzy continuous
mapping,intuitionistic fuzzy bounded linear operator
http://ijfs.usb.ac.ir/article_361.html
http://ijfs.usb.ac.ir/article_361_9c78a3a61bf8f44f026f0d019b06cef9.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
4
1
2007
04
30
Persian-translation Vol.4, No.1 April 2007
105
111
EN
10.22111/ijfs.2007.2911
http://ijfs.usb.ac.ir/article_2911.html
http://ijfs.usb.ac.ir/article_2911_64fd55c1f871dba2e12a5205e4b5b3d1.pdf