eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2007-04-30
4
1
0
10.22111/ijfs.2007.2910
2910
Cover Vol.4, No.1 April 2007
http://ijfs.usb.ac.ir/article_2910_2e784435556c4da339b26e553e994ebf.pdf
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2007-04-09
4
1
1
19
10.22111/ijfs.2007.353
353
مقاله پژوهشی
DISTRIBUTED AND COLLABORATIVE FUZZY MODELING
WITOLD PEDRYCZ
pedrycz@ee.ualberta.ca
1
DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING, UNIVERSITY OF ALBERTA, EDMONTON T6R 2G7 CANADA AND SYSTEMS RESEARCH INSTITUTE OF THE POLISH ACADEMY OF SCIENCE, WARSAW, POLAND
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.
http://ijfs.usb.ac.ir/article_353_e16a2e92cedec4f2fbb09d7c81a6cb72.pdf
Computational Intelligence
C^{3} paradigm
Distributed processing
Fuzzy
clustering
Fuzzy models
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2007-04-09
4
1
21
36
10.22111/ijfs.2007.355
355
مقاله پژوهشی
USING DISTRIBUTION OF DATA TO ENHANCE PERFORMANCE OF FUZZY CLASSIFICATION SYSTEMS
EGHBAL G. MANSOORI
mansoori@shirazu.ac.ir
1
MANSOOR J. ZOLGHADRI
zjahromi@shirazu.ac.ir
2
SERAJ D. KATEBI
katebi@shirazu.ac.ir
3
COMPUTER SCIENCE AND ENGINEERING DEPARTMENT, COLLEGE OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
COMPUTER SCIENCE AND ENGINEERING DEPARTMENT, COLLEGE OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
COMPUTER SCIENCE AND ENGINEERING DEPARTMENT, COLLEGE OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
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.
http://ijfs.usb.ac.ir/article_355_8012934623da6f85204a71c666c34242.pdf
Fuzzy rule-based classification systems
Rule weight
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2007-04-09
4
1
37
51
10.22111/ijfs.2007.356
356
مقاله پژوهشی
FUZZY BASED FAULT DETECTION AND CONTROL FOR 6/4 SWITCHED RELUCTANCE MOTOR
N. SELVAGANESAN
n_selvag@rediffmail.com
1
D. RAJA
2
S. SRINIVASAN
srini@mitindia.edu
3
DEPARTMENT OF ELECTRICAL & ELECTRONICS ENGINEERING, PONDICHERRY ENGINEERING COLLEGE, PONDICHERRY-605014, INDIA
DEPARTMENT OF ELECTRICAL & ELECTRONICS ENGINEERING, PONDICHERRY ENGINEERING COLLEGE, PONDICHERRY-605014, INDIA
DEPARTMENT OF INSTRUMENTATION ENGINEERING, MIT CAMPUS, ANNA UNIVERSITY, CHROMEPET, CHENNAI-600044, INDIA
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.
http://ijfs.usb.ac.ir/article_356_9057931d5fc7d9338fab8be76e173fb2.pdf
Fault Diagnosis
Fuzzy logic
Switched Reluctance Motor
Fuzzy
Inference Systems
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2007-04-09
4
1
53
64
10.22111/ijfs.2007.357
357
مقاله پژوهشی
SOME RESULTS ON INTUITIONISTIC FUZZY SPACES
S. B. Hosseini
1
Donal O’Regan
donal.oregan@nuigalway.ie
2
Reza Saadati
rsaadati@eml.cc
3
Islamic Azad University-Nour Branch, Nour, Iran
Department of Mathematics, National University of Ireland, Galway, Ireland
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
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.
http://ijfs.usb.ac.ir/article_357_3141e238301eb28b17f345772521dbda.pdf
Intuitionistic fuzzy metric (normed) spaces
Completeness
Compactness
Finite dimensional
Weak convergence
Quotient spaces
Approximation theory
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2007-04-09
4
1
65
73
10.22111/ijfs.2007.358
358
مقاله پژوهشی
L-FUZZY BILINEAR OPERATOR AND ITS CONTINUITY
Cong-hua Yan
chyan@njnu.edu.cn
1
Jin-xuan Fang
jxfang@njnu.edu.cn
2
Department of Mathematics, Nanjing Normal University, Nanjing Jiangsu, 210097, P.R.China
Department of Mathematics, Nanjing Normal University, Nanjing Jiangsu, 210097, P.R.China
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.
http://ijfs.usb.ac.ir/article_358_af8602c55b74198bfce9a442e63cec84.pdf
Order-homomorphism
Powerset operator
L-bilinear operator
Molecule
net
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2007-04-09
4
1
75
87
10.22111/ijfs.2007.359
359
مقاله پژوهشی
TRIANGULAR FUZZY MATRICES
Amiya Kumar l Shyama
1
Madhumangal Pal
madhumangal@lycos.com
2
Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore - 721102, West Bengal, India
Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore - 721102, West Bengal, India
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.
http://ijfs.usb.ac.ir/article_359_0038d4fb0f550de224041cbbbd77caf6.pdf
Triangular fuzzy numbers
Triangular fuzzy number arithmetic
Triangular
fuzzy matrices
Triangular fuzzy determinant
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2007-04-09
4
1
89
101
10.22111/ijfs.2007.361
361
مقاله پژوهشی
INTUITIONISTIC FUZZY BOUNDED LINEAR OPERATORS
S. Vijayabalaji
balaji−nandini@rediffmail.com
1
N. Thillaigovindan
thillai−n@sify.com
2
Department of Mathematics, Annamalai University, Annamalainagar- 608002, Tamilnadu, India
Department of Mathematics Section, Faculty of Engineering and Technology, Annamalai University, Annamalainagar-608002, Tamilnadu, India
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.
http://ijfs.usb.ac.ir/article_361_9c78a3a61bf8f44f026f0d019b06cef9.pdf
fuzzy n-norm
intuitionistic fuzzy n-norm
intuitionistic fuzzy continuous
mapping
intuitionistic fuzzy bounded linear operator
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2007-04-30
4
1
105
111
10.22111/ijfs.2007.2911
2911
Persian-translation Vol.4, No.1 April 2007
http://ijfs.usb.ac.ir/article_2911_64fd55c1f871dba2e12a5205e4b5b3d1.pdf