University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
30
Cover Special Issue vol. 13, no. 7, Decemberr 2016
0
EN
10.22111/ijfs.2016.2949
http://ijfs.usb.ac.ir/article_2949.html
http://ijfs.usb.ac.ir/article_2949_c2a2d61733497224fc24c7f242f74085.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
31
SYSTEM MODELING WITH FUZZY MODELS: FUNDAMENTAL DEVELOPMENTS AND PERSPECTIVES
1
14
EN
WITOLD
PEDRYCZ
DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING
UNIVERSITY OF ALBERTA EDMONTON T6R 2V4 AB CANADA, DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING FACULTY OF ENGINEERING KING ABDULAZIZ
UNIVERSITY JEDDAH, 21589 SAUDI ARABIA AND SYSTEMS RESEARCH INSTITUTE POLISH
ACADEMY OF SCIENCES, WARSAW POLAND.
pedrycz@ee.ualberta.ca
10.22111/ijfs.2016.2940
In this study, we offer a general view at the area of fuzzy modeling and fuzzymodels, identify the visible development phases and elaborate on a new and promisingdirections of system modeling by introducing a concept of granular models. Granularmodels, especially granular fuzzy models constitute an important generalization of existingfuzzy models and, in contrast to the existing models, generate results in the form ofinformation granules (such as intervals, fuzzy sets, rough sets and others). We present arationale and deliver some key motivating arguments behind the emergence of granularmodels and discuss their underlying design process. Central to the development of granularmodels are granular spaces, namely a granular space of parameters of the models and agranular input space. The development of the granular model is completed through anoptimal allocation of information granularity, which optimizes criteria of coverage andspecificity of granular information. The emergence of granular models of type-2 and type-n,in general, is discussed along with an elaboration on their formation. It is shown thatachieving a sound coverage-specificity tradeoff (compromise) is of paramount relevance inthe realization of the granular models.
Fuzzy models,Granular computing,information granules of higher type,Granular spaces
http://ijfs.usb.ac.ir/article_2940.html
http://ijfs.usb.ac.ir/article_2940_16d7b0c0bed5ba69ff2b3b46e7f4336c.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
30
ON THE COMPATIBILITY OF A CRISP RELATION WITH A FUZZY EQUIVALENCE RELATION
15
31
EN
B. De
Baets
KERMIT, Department of Mathematical Modelling, Statistics and
Bioinformatics, Ghent University, Coupure links 653, B-9000, Gent, Belgium
H.
Bouremel
Department of Mathematics, Faculty of Mathematics and Informatics,
Med Boudiaf University of Msila, P.O. Box 166 Ichbilia, Msila 28000, Algeria
L.
Zedam
Department of Mathematics, Faculty of Mathematics and Informatics,
Med Boudiaf University of Msila, P.O. Box 166 Ichbilia, Msila 28000, Algeria
l.zedam@yahoo.fr
10.22111/ijfs.2016.2941
In a recent paper, De Baets et al. have characterized the fuzzytolerance and fuzzy equivalence relations that a given strict order relation iscompatible with. In this paper, we generalize this characterization by consideringan arbitrary (crisp) relation instead of a strict order relation, while payingattention to the particular cases of a reflexive or irreflexive relation. The reasoninglargely draws upon the notion of the clone relation of a (crisp) relation,introduced recently by Bouremel et al., and the partition of this clone relationin terms of three different types of pairs of clones. More specifically, reflexive related clones and irreflexive unrelated clones turn out to play a key role in thecharacterization of the fuzzy tolerance and fuzzy equivalence relations that agiven (crisp) relation is compatible with.
Crisp relation,Fuzzy relation,Clone relation,Compatibility,Tolerance relation,Equivalence relation
http://ijfs.usb.ac.ir/article_2941.html
http://ijfs.usb.ac.ir/article_2941_e66348aeee3c2b2b3fb70d708b5956cd.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
31
DC-DC CONVERTER WITH FUZZY CONTROLLER FOR SOLAR CELL APPLICATIONS ON MOBILE ROBOTS
33
52
EN
J.
Cruz-Lambert
Electrical and Computer Engineering Department, The University
of Texas at San Antonio, San Antonio, Texas, USA
P.
Benavidez
Electrical and Computer Engineering Department, The University
of Texas at San Antonio, San Antonio, Texas, USA
J.
Ortiz
Electrical and Computer Engineering Department, The University of Texas
at San Antonio, San Antonio, Texas, USA
N.
Gallardo
Electrical and Computer Engineering Department, The University of
Texas at San Antonio, San Antonio, Texas, USA
B. A.
Erol
Electrical and Computer Engineering Department, The University of
Texas at San Antonio, San Antonio, Texas, USA
J.
Richey
Electrical and Computer Engineering Department, The University of
Texas at San Antonio, San Antonio, Texas, USA
S.
Morris
Electrical and Computer Engineering Department, The University of
Texas at San Antonio, San Antonio, Texas, USA
M.
Jamshidi
Electrical and Computer Engineering Department, The University of
Texas at San Antonio, San Antonio, Texas, USA
10.22111/ijfs.2016.2942
Emerging technologies increase the needs on self efficient mobile robotic applications that bring a new concern of sustainable and continuous power supply for the robotic platforms. This paper covers the various techniques and technologies used to design a solar powered robot, exploring the currently available products, software and limitations to this application. The main aim is to integrate a fuzzy logic based charging system which allows the batteries to be charged from solar panels, wall outlet, and a deploy-able solar charging station. The goal of this paper is to summarize the tested methods and results to expedite future researchers in the correct direction. This paper will cover only up to the design of the DC-DC converter and simulation, as further work is still pending implementation on actual hardware.Simulations results are provided to evaluate the feasibility of the paper for future implementations.
Solar,Renewable,LiPo,Lithium Polymer,MPPT,Robotics,Fuzzy controller,Energy
http://ijfs.usb.ac.ir/article_2942.html
http://ijfs.usb.ac.ir/article_2942_93977f91069127aa27550c83972e06e9.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
30
A NOTE TO INTERPRETABLE FUZZY MODELS AND THEIR LEARNING
53
65
EN
Vilem
Novak
University of Ostrava, Institute for Research and Applications of
Fuzzy Modeling, NSC IT4Innovations, 30. dubna 22, 701 03 Ostrava 1, Czech Republic
10.22111/ijfs.2016.2943
In this paper we turn the attention to a well developed theory of fuzzy/lin-guis-tic models that are interpretable and, moreover, can be learned from the data.We present four different situations demonstrating both interpretability as well as learning abilities of these models.
Fuzzy Natural Logic,Perception-based logical deduction,Learning. } newlineindent{footnotesize {The paper has been supported by the project IT4I XS (LQ1602)
http://ijfs.usb.ac.ir/article_2943.html
http://ijfs.usb.ac.ir/article_2943_96ab638fc5e0fd03be6c8ba7e35c5e6f.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
30
MINING FUZZY TEMPORAL ITEMSETS WITHIN VARIOUS TIME INTERVALS IN QUANTITATIVE DATASETS
67
89
EN
Mahnaz
Kadkhoda
Department of Computer Engineering, Center of Excellence
on Soft Computing and Intelligent Information Processing, Ferdowsi University of
Mashhad, Mashhad, Iran
Mohammad-R.
Akbarzadeh-T
Department of Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran
S. Mahmoud
Taheri
Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran
taher@cc.iut.ac.ir;sm_taheri@ut.ac.ir
10.22111/ijfs.2016.2944
This research aims at proposing a new method for discovering frequent temporal itemsets in continuous subsets of a dataset with quantitative transactions. It is important to note that although these temporal itemsets may have relatively high textit{support} or occurrence within particular time intervals, they do not necessarily get similar textit{support} across the whole dataset, which makes it almost impossible to extract them using the existing traditional algorithms. This paper directly addresses this problem and introduces a new algorithm called Fuzzy Solid Linguistic Itemset Mining (FSLIM) to discover Solid Linguistic Itemsets (SLIs) within a quantitative dataset. SLI is a new concept introduced here as an essential part of the solution presented in this paper. The proposed method consists of two phases. In the first phase, fuzzy set theory is used to transform each quantitative value to a linguistic item; and in the second phase, all SLIs are extracted. Finally, the efficiency of FSLIM is compared in terms of execution time, scalability and the number of frequent patterns with those of two classic approaches on synthetic datasets. The proposed approach is also applied to an actual Mashhad Urban Traffic dataset in order to illustrate FSLIM's ability in discovering the hidden knowledge that could not be extracted by traditional methods.
Fuzzy data mining,Temporal data mining,Frequent itemset,Temporal quantitative dataset
http://ijfs.usb.ac.ir/article_2944.html
http://ijfs.usb.ac.ir/article_2944_d38c9bdaf4139b353082432c484adc12.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
30
SOLUTION-SET INVARIANT MATRICES AND VECTORS IN FUZZY RELATION INEQUALITIES BASED ON MAX-AGGREGATION FUNCTION COMPOSITION
91
100
EN
F.
Kouchakinejad
Department of Mathematics, Graduate University of Advanced
Technology, Kerman, Iran
M.
Mashinchi
Department of Statistics, Faculty of Mathematics and Computer Sciences, Shahid Bahonar University of Kerman, Kerman, Iran
ijfs-editor@usb.ac.ir
R.
Mesiar
Slovak University of Technology in Bratislava, Faculty of Civil Engineering, Radlinskeho 11, 810 05 Bratislava, Slovak Republic
10.22111/ijfs.2016.2945
Fuzzy relation inequalities based on max-F composition are discussed, where F is a binary aggregation on [0,1]. For a fixed fuzzy relation inequalities system $ A circ^{F}textbf{x}leqtextbf{b}$, we characterize all matrices $ A^{'} $ For which the solution set of the system $ A^{' } circ^{F}textbf{x}leqtextbf{b}$ is the same as the original solution set. Similarly, for a fixed matrix $ A $, the possible perturbations $ b^{'} $ of the right-hand side vector $ b $ not modifying the original solution set are determined. Several illustrative examples are included to clarify the results of the paper.
Aggregation function,Max-aggregation function composition,Solution-set invariant matrices,Solution-set invariant vectors,System of fuzzy relation inequalities
http://ijfs.usb.ac.ir/article_2945.html
http://ijfs.usb.ac.ir/article_2945_c340fe680c20557fc27f32ba5cc9cf8f.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
30
AN OBSERVER-BASED INTELLIGENT DECENTRALIZED VARIABLE STRUCTURE CONTROLLER FOR NONLINEAR NON-CANONICAL NON-AFFINE LARGE SCALE SYSTEMS
101
130
EN
REZA
GHASEMI
DEPARTMENT OF ELECTRICAL ENGINEERING, UNIVERSITY OF QOM, QOM, IRAN
MOHAMMAD BAGHER
MENHAJ
DEPARTMENT OF ELECTRICAL ENGINEERING, AMIRKABIR UNIVERSITY
OF TECHNOLOGY, TEHRAN, IRAN, AND QIAU’S INCUBATOR CENTER OF TECHNOLOGY UNITS (CENTER
OF COGNITIVE SYSTEMS), QAZVIN, IRAN
10.22111/ijfs.2016.2946
In this paper, an observer based fuzzy adaptive controller (FAC) is designed fora class of large scale systems with non-canonical non-affine nonlinear subsystems. It isassumed that functions of the subsystems and the interactions among subsystems areunknown. By constructing a new class of state observer for each follower, the proposedconsensus control method solves the problem of unmeasured states of nonlinear noncanonicalnon-affine subsystems. The main characteristics of the proposed observer-basedintelligent controller are: 1) on-line adaptation of the controller and the observer parameters,2) ultimate boundedness of both the output and the observer errors, 3) boundedness of allsignals involved, 4) employing experts’ knowledge in the controller design procedure and 5)chattering avoidance. The simulation results are further carried out to demonstrate better theeffectiveness of the proposed fuzzy based consensus controller method.
Lyapunov Stability,Adaptive control,Non-affine nonlinear system,large scale systems,Fuzzy systems,Nonlinear observer
http://ijfs.usb.ac.ir/article_2946.html
http://ijfs.usb.ac.ir/article_2946_548f6d22a30e721cee9755ec84977197.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
30
MINIMAL AND STATEWISE MINIMAL INTUITIONISTIC GENERAL L-FUZZY AUTOMATA
131
152
EN
M.
Shamsizadeh
Department of Mathematics, Graduate University of Advanced
Technology, Kerman, Iran
M. M.
Zahedi
Department of Mathematics, Graduate University of Advanced Tech-
nology, Kerman, Iran
zahedi_mm@ mail.uk.ac.ir
10.22111/ijfs.2016.2947
In this note, by considering the notions of the intuitionistic general L-fuzzy automaton and $(alpha, beta)$-language, we show that for any $(alpha, beta)$-language $mathcal{L}$, there exists a minimal intuitionistic general L-fuzzy automaton recognizing $mathcal{L}$.We prove that the minimal intuitionistic general L-fuzzy automaton is isomorphic with threshold $(alpha,beta)$ to any $(alpha, beta)$-reduced max-min intuitionistic general L-fuzzy automaton.%Also, we prove that the minimal intuitionistic general L-fuzzy automaton is an $(alpha, beta)$-reduced.Also, we show that for any strong deterministic max-min intuitionistic general L-fuzzy automaton there exists a statewise $(alpha, beta)$-minimal intuitionistic general L-fuzzy automaton.In particular, a connection between the minimal and statewise $(alpha, beta)$-minimal intuitionistic general L-fuzzy automaton is presented.%We show if $tilde{F}^*$ is an $(alpha, beta)$-complete $(alpha, beta)$-accessible deterministic max-min intuitionistic general L-fuzzy automaton and it is recognizing $(alpha, beta)$-language $mathcal{L}$, then the minimal $tilde{F}^*_{mathcal{L}}$ is homomorphism with threshold $(alpha, beta)$ to statewise $(alpha, beta)$-minimal $tilde{F}_{m}^*$, where $tilde{F}_{m}^*$ is statewise $(alpha, beta)$-equivalent to $tilde{F}^*$.Also, for a given intuitionistic general L-fuzzy automaton, we present two algorithms, which determinesstates of the minimal intuitionistic general L-fuzzy automaton and the statewise $(alpha, beta)$-minimal intuitionistic general L-fuzzy automaton.Finally, by giving some examples, we comparison minimal intuitionistic general L-fuzzy automaton and statewise $(alpha, beta)$-minimal intuitionistic general L-fuzzy automaton.
Minimal automata,$(alpha,beta)$-language,Statewise minimal automata,Homomorphism with threshold $(alpha,beta)$
http://ijfs.usb.ac.ir/article_2947.html
http://ijfs.usb.ac.ir/article_2947_e25316858488812f742b91e5709605c4.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
30
SOFT TOPOLOGY AND SOFT PROXIMITY AS FUZZY PREDICATES BY FORMULAE OF LUKASIEWICZ LOGIC
153
168
EN
O. R.
Sayed
Department of Mathematics, Faculty of Science, Assiut University,
Assiut, Egypt
R. A.
Borzooei
Department of Mathematics, Shahid Beheshti University, Tehran,
Iran
borzooei@sbu.ac.ir
10.22111/ijfs.2016.2948
In this paper, based in the L ukasiewicz logic, the definition offuzzifying soft neighborhood structure and fuzzifying soft continuity areintroduced. Also, the fuzzifying soft proximity spaces which are ageneralizations of the classical soft proximity spaces are given. Severaltheorems on classical soft proximities are special cases of the theorems weprove in this paper.
Soft set,Soft topology,Fuzzifying soft topology,Fuzzifying soft proximity
http://ijfs.usb.ac.ir/article_2948.html
http://ijfs.usb.ac.ir/article_2948_ecf1fe1f138a39e8d7a8dd747ecdb98f.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
13
7
2016
12
30
Persian-translation vol. 13, no. 7, Decemberr 2016
171
179
EN
10.22111/ijfs.2016.2950
http://ijfs.usb.ac.ir/article_2950.html
http://ijfs.usb.ac.ir/article_2950_655a94a8efc837e6b4e0c64d101dd333.pdf