University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
29
Cover vol. 14, no. 5, October 2017
0
EN
10.22111/ijfs.2017.3438
http://ijfs.usb.ac.ir/article_3438.html
http://ijfs.usb.ac.ir/article_3438_c321127867045c0b7691270c858d873b.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
30
A NOVEL FUZZY-BASED SIMILARITY MEASURE FOR COLLABORATIVE FILTERING TO ALLEVIATE THE SPARSITY PROBLEM
1
18
EN
Masoud
Saeed
School of Electrical and Computer Engineering, Shiraz University,
Shiraz, Iran
msaeedmz@gmail.com
Eghbal G
Mansoori
School of Electrical and Computer Engineering, Shiraz University,
Shiraz, Iran
10.22111/ijfs.2017.3429
Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based on fuzzy set theory along with default voting technique aimed to provide a valid similarity measurement between users wherever the available ratings are relatively rare. The main idea of this research is to model the rating behaviour of each user by a fuzzy set, and use this model to determine the user's degree of interest on items. Experimental results on the MovieLens and Netflix datasets show the effectiveness of the proposed algorithm in handling data sparsity problem. It also outperforms some state-of-the-art collaborative filtering algorithms in terms of prediction quality.
Recommender system,Collaborative filtering,Similarity measure,Data sparsity
http://ijfs.usb.ac.ir/article_3429.html
http://ijfs.usb.ac.ir/article_3429_9dd80fbae48d72d9c3e7414611160480.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
29
DISTINGUISHABILITY AND COMPLETENESS OF CRISP DETERMINISTIC FUZZY AUTOMATA
19
30
EN
Renu
.
Indian School of Mines, Dhanbad, India
renuismmaths@gmail.com
S. P.
Tiwari
Department of Applied Mathematics, Indian Institute of Technology
(ISM), Dhanbad-826004, India
10.22111/ijfs.2017.3430
In this paper, we introduce and study notions like state-\linebreak distinguishability, input-distinguishability and output completeness of states of a crisp deterministic fuzzy automaton. We show that for each crisp deterministic fuzzy automaton there corresponds a unique (up to isomorphism), equivalent distinguished crisp deterministic fuzzy automaton. Finally, we introduce two axioms related to output completeness of states and discuss the interrelationship between them.
Crisp deterministic fuzzy automaton,Indistinguishable states,Input-indistinguishable,Homomorphism,Output complete
http://ijfs.usb.ac.ir/article_3430.html
http://ijfs.usb.ac.ir/article_3430_47258f258c4b3466bedcf1c4d7d39c8a.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
29
BATHTUB HAZARD RATE DISTRIBUTIONS AND FUZZY LIFE TIMES
31
41
EN
Muhammad
Shafiq
Institute of Statistics and Mathematical Methods in Economics,
Vienna University of Technology, Wien, Austria
shafiq@kust.edu.pk
Reinhard
Viertl
Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Wien, Austria
r.viertl@tuwien.ac.at
10.22111/ijfs.2017.3431
The development of life time analysis started back in the $20^{textit{th}}$ century and since then comprehensive developments have been made to model life time data efficiently. Recent development in measurements shows that all continuous measurements can not be measured as precise numbers but they are more or less fuzzy. Life time is also a continuous phenomenon, and has already been shown that life time observations are not precise measurements but fuzzy. Therefore, the corresponding analysis techniques employed on the data require to consider fuzziness of the observations to obtain appropriate estimates.In this study generalized estimators for the parameters and hazard rates are proposed for bathtub failure rate distributions to model fuzzy life time data effectively.
Bathtub failure rate,Fuzzy number,Life time,Non-precise data
http://ijfs.usb.ac.ir/article_3431.html
http://ijfs.usb.ac.ir/article_3431_9cc7cc95245cf4eede6534e9ece4735e.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
01
ADAPTIVE FUZZY OUTPUT FEEDBACK TRACKING CONTROL FOR A CLASS OF NONLINEAR TIME-VARYING DELAY SYSTEMS WITH UNKNOWN BACKLASH-LIKE HYSTERESIS
43
64
EN
Mohsen
Hasanpour Naseriyeh
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
mohsen.hasanpour@eng.uk.ac.ir
Adeleh
Arabzadeh Jafari
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
aarabzadeh@eng.uk.ac.ir
Seyed Mohammad Ali
Mohammadi
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
a_mohammadi@uk.ac.ir
10.22111/ijfs.2017.3432
This paper considers the problem of adaptive output feedback tracking control for a class of nonstrict-feedback nonlinear systems with unknown time-varying delays and unknown backlash-like hysteresis. Fuzzy logic systems are used to estimate the unknown nonlinear functions. Based on the Lyapunov–Krasovskii method, the control scheme is constructed by using the backstepping and adaptive technique. The proposed adaptive controller guarantees that all the closed-loop signals are semiglobally uniformly ultimately bounded and the tracking error can converge to a small neighborhood of the origin. Finally, Simulation results further show the effectiveness of the proposed approach.
Adaptive fuzzy control,Backstepping design technique,Backlash-like hysteresis,Nonstrict-feedback form,Nonlinear control
http://ijfs.usb.ac.ir/article_3432.html
http://ijfs.usb.ac.ir/article_3432_cc814b436b91bfe3ef40ebdb32c04112.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
29
K-FLAT PROJECTIVE FUZZY QUANTALES
65
81
EN
Jing
Lu
College of Mathematics and Information Science, Shaanxi Normal Univer-
sity, Xi'an 710119, P.R. China
1044250817@qq.com
Kaiyun
Wang
College of Mathematics and Information Science, Shaanxi Normal
University, Xi'an 710119, P.R. China
wangkaiyun@snnu.edu.cn
Bin
Zhao
College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, P.R. China
zhaobin@snnu.edu.cn
10.22111/ijfs.2017.3433
In this paper, we introduce the notion of {bf K}-flat projective fuzzy quantales, and give an elementary characterization in terms of a fuzzy binary relation on the fuzzy quantale. Moreover, we prove that {bf K}-flat projective fuzzy quantales are precisely the coalgebras for a certain comonad on the category of fuzzy quantales. Finally, we present two special cases of {bf K} as examples.
Fuzzy quantale,Fuzzy binary relation,{bf K}-flat projective fuzzy quantale,Comonad
http://ijfs.usb.ac.ir/article_3433.html
http://ijfs.usb.ac.ir/article_3433_72d18bb9c00190de1a6790f1395bafc0.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
30
L-FUZZY CONVEXITY INDUCED BY L-CONVEX FUZZY SUBLATTICE DEGREE
83
102
EN
Juan
Li
School of Mathematics, Beijing Institute of Technology, Beijing 100081,
PR China
lijuan@htu.edu.cn
Fu Gui
Shi
School of Mathematics, Beijing Institute of Technology, Beijing 100081,
PR China
10.22111/ijfs.2017.3434
In this paper, the notion of $L$-convex fuzzy sublattices is introduced and their characterizations are given. Furthermore, the notion of the degree to which an $L$-subset is an $L$-convex fuzzy sublattice is proposed and its some characterizations are given. Besides, the $L$-convex fuzzy sublattice degrees of the homomorphic image and pre-image of an $L$-subset are studied. Finally, we obtain an $L$-fuzzy convexity, which is induced by the $L$-convex fuzzy sublattice degrees, in the sense of Shi and Xiu.
$L$-convex fuzzy sublattice,Implication operator,$L$-convex fuzzy sublattice degree,$L$-fuzzy convexity
http://ijfs.usb.ac.ir/article_3434.html
http://ijfs.usb.ac.ir/article_3434_b495c3e9632ab62dfe43bbdb7a7956a4.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
30
GENERAL FUZZY AUTOMATA BASED ON COMPLETE RESIDUATED LATTICE-VALUED
103
121
EN
K.
Abolpour
Department of Mathematics, Kazerun Branch, Islamic Azad University, Kazerun, Iran
M. M.
Zahedi
Department of Mathematics, Kerman Graduate University of Advanced Technology, Kerman, Iran
zahedi_mm@ mail.uk.ac.ir
10.22111/ijfs.2017.3435
The present paper has been an attempt to investigate the general fuzzy automata on the basis of complete residuated lattice-valued ($L$-GFAs). The study has been chiefly inspired from the work by Mockor cite{15, 16, 17}. Regarding this, the categorical issue of $L$-GFAs has been studied in more details. The main issues addressed in this research include: (1) investigating the relationship between the category of $L$-GFAs and the category of non-deterministic automata (NDAs); as well as the relationship between the category of generalized $L$-GFAs and the category of NDAs; (2) demonstrating the existence of isomorphism between the category of $L$-GFAs and the subcategory of generalized $L$-GFAs and between the category of $L$-GFAs and the category of sets of NDAs; (3) and further scrutinizing some specific relationship between the output $L$-valued subsets of generalized $L$-GFAs and the output $L$-valued of NDAs.
General fuzzy automata,Active state set,Residuated-lattice,Isomorphism of category,Functor
http://ijfs.usb.ac.ir/article_3435.html
http://ijfs.usb.ac.ir/article_3435_d7de60bfd33f9d7c4b580919e282781c.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
30
SOME COUPLED FIXED POINT RESULTS ON MODIFIED INTUITIONISTIC FUZZY METRIC SPACES AND APPLICATION TO INTEGRAL TYPE CONTRACTION
123
137
EN
Vishal
Gupta
Department of Mathematics, Maharishi Markandeshwar University,
Mullana-133207, Ambala, Haryana, India
vishal.gmn@gmail.com
Rajesh
Kumar Saini
Department of Mathematics, Statistics and Computer Applications, Bundelkhand University, Jhansi, U.P., India
Ashima
Kanwar
Department of Mathematics, Maharishi Markandeshwar University,
Mullana-133207, Ambala, Haryana, India
kanwar.ashima87@gmail.com
10.22111/ijfs.2017.3436
In this paper, we introduce fruitful concepts of common limit range and joint common limit range for coupled mappings on modified intuitionistic fuzzy metric spaces. An illustrations are also given to justify the notion of common limit range and joint common limit range property for coupled maps. The purpose of this paper is to prove fixed point results for coupled mappings on modified intuitionistic fuzzy metric spaces. Moreover, we extend the notion of common limit range property and E.A property for coupled maps on modified intuitionistic fuzzy metric spaces. As an application, we extend our main result to integral type contraction condition and also for finite number of mappings on modified intuitionistic fuzzy metric spaces.
Modified intuitionistic fuzzy metric space (MIFM-space),Coupled maps,Common limit range property,Joint common limit range property,E.A property,Weakly compatible mappings
http://ijfs.usb.ac.ir/article_3436.html
http://ijfs.usb.ac.ir/article_3436_fff7b56affd28a047b3d02fce1c85f32.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
30
INTERVAL ANALYSIS-BASED HYPERBOX GRANULAR COMPUTING CLASSIFICATION ALGORITHMS
139
156
EN
Hongbing
Liu
Center of Computing, Xinyang Normal University, Xinyang 464000,
P. R. China
liuhbing@126.com
Jin
Li
Center of Computing, Xinyang Normal University, Xinyang 464000, P. R. China
lijin@xynu.edu.cn
Huaping
Guo
School of Computer and Information Technology, Xinyang Normal
University, Xinyang 464000, P. R. China
hpguo_cm@163.com
Chunhua
Liu
School of Computer and Information Technology, Xinyang Normal
University, Xinyang 464000, P. R. China
zzdxliuch@163.com
10.22111/ijfs.2017.3437
Representation of a granule, relation and operation between two granules are mainly researched in granular computing. Hyperbox granular computing classification algorithms (HBGrC) are proposed based on interval analysis. Firstly, a granule is represented as the hyperbox which is the Cartesian product of $N$ intervals for classification in the $N$-dimensional space. Secondly, the relation between two hyperbox granules is measured by the novel positive valuation function induced by the two endpoints of an interval, where the operations between two hyperbox granules are designed so as to include granules with different granularity. Thirdly, hyperbox granular computing classification algorithms are designed on the basis of the operations between two hyperbox granules, the fuzzy inclusion relation between two hyperbox granules, and the granularity threshold. We demonstrate the superior performance of the proposed algorithms compared with the traditional classification algorithms, such as, Random Forest (RF), Support Vector Machines (SVMs), and Multilayer Perceptron (MLP).
Fuzzy lattice,Granular computing,Hyperbox granule,Fuzzy inclusion relation
http://ijfs.usb.ac.ir/article_3437.html
http://ijfs.usb.ac.ir/article_3437_aafac877f889b32648b9815b14c238e2.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
14
5
2017
10
30
Persian-translation vol. 14, no. 5, October 2017
159
167
EN
10.22111/ijfs.2017.3439
http://ijfs.usb.ac.ir/article_3439.html
http://ijfs.usb.ac.ir/article_3439_736c38cb31e5500e70b8efba086f3d16.pdf