ORIGINAL_ARTICLE
Cover vol. 11, no. 1, February 2014
http://ijfs.usb.ac.ir/article_2691_0de979703efb6c6126122e5c4eafa558.pdf
2014-03-01T11:23:20
2018-10-24T11:23:20
0
10.22111/ijfs.2014.2691
ORIGINAL_ARTICLE
ADAPTIVE FUZZY TRACKING CONTROL FOR A CLASS OF NONLINEAR SYSTEMS WITH UNKNOWN DISTRIBUTED TIME-VARYING DELAYS AND UNKNOWN CONTROL DIRECTIONS
In this paper, an adaptive fuzzy control scheme is proposed for a class of perturbed strict-feedback nonlinear systems with unknown discrete and distributed time-varying delays, and the proposed design method does not require a priori knowledge of the signs of the control gains.Based on the backstepping technique, the adaptive fuzzy controller is constructed. The main contributions of the paper are that (i) by constructing appropriate Lyapunov functionals and using the Nussbaum functions, the adaptive tracking control problem is solved for the strict-feedback unknown nonlinear systems with the unknown discrete and distributed time-varying delays and the unknown control directions (ii) the number of adaptive parameters is independent of the number of rules of fuzzy logic systems and system state variables, which reduces the computation burden greatly. It is proven that the proposed controller guaranteesthat all the signals in the closed-loop system are bounded and the system output converges to a small neighborhood of the desired reference signal. Finally, an example is used to show the effectiveness of theproposed approach.
http://ijfs.usb.ac.ir/article_1393_5998db6dda90762fa01867861621467b.pdf
2014-02-24T11:23:20
2018-10-24T11:23:20
1
25
10.22111/ijfs.2014.1393
Fuzzy adaptive control
Nonlinear systems
Discrete and distributed time-varying delays
Backstepping
Lyapunov-Krasovskii functionals
Hongyun
Yue
yuehongyun0417@163.com
true
1
Department of Applied Mathematics, Xidian University, Xi'an 710071,
P.R.China
Department of Applied Mathematics, Xidian University, Xi'an 710071,
P.R.China
Department of Applied Mathematics, Xidian University, Xi'an 710071,
P.R.China
AUTHOR
Junmin
Li
jmli@mail.xidian.edu.cn
true
2
Department of Applied Mathematics, Xidian University, Xi'an 710071,
P.R.China
Department of Applied Mathematics, Xidian University, Xi'an 710071,
P.R.China
Department of Applied Mathematics, Xidian University, Xi'an 710071,
P.R.China
LEAD_AUTHOR
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{it Direct adaptive fuzzy control for nonlinear systems with time-varying delays},
14
Information Sciences, {bf 180}textbf{(5)} (2010), 776-792.
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102
ORIGINAL_ARTICLE
Comparing different stopping criteria for fuzzy decision tree induction through IDFID3
Fuzzy Decision Tree (FDT) classifiers combine decision trees with approximate reasoning offered by fuzzy representation to deal with language and measurement uncertainties. When a FDT induction algorithm utilizes stopping criteria for early stopping of the tree's growth, threshold values of stopping criteria will control the number of nodes. Finding a proper threshold value for a stopping criterion is one of the greatest challenges to be faced in FDT induction. In this paper, we propose a new method named Iterative Deepening Fuzzy ID3 (IDFID3) for FDT induction that has the ability of controlling the tree’s growth via dynamically setting the threshold value of stopping criterion in an iterative procedure. The final FDT induced by IDFID3 and the one obtained by common FID3 are the same when the numbers of nodes of induced FDTs are equal, but our main intention for introducing IDFID3 is the comparison of different stopping criteria through this algorithm. Therefore, a new stopping criterion named Normalized Maximum fuzzy information Gain multiplied by Number of Instances (NMGNI) is proposed and IDFID3 is used for comparing it against the other stopping criteria. Generally speaking, this paper presents a method to compare different stopping criteria independent of their threshold values utilizing IDFID3. The comparison results show that FDTs induced by the proposed stopping criterion in most situations are superior to the others and number of instances stopping criterion performs better than fuzzy information gain stopping criterion in terms of complexity (i.e. number of nodes) and classification accuracy. Also, both tree depth and fuzzy information gain stopping criteria, outperform fuzzy entropy, accuracy and number of instances in terms of mean depth of generated FDTs.
http://ijfs.usb.ac.ir/article_1394_2ba9daf85e10827d238087b91e686c5c.pdf
2014-02-25T11:23:20
2018-10-24T11:23:20
27
48
10.22111/ijfs.2014.1394
Fuzzy inference systems
Classification
Fuzzy decision tree
Stopping criteria
Mohsen
Zeinalkhani
zeinalkhani@gmail.com
true
1
Department of Computer Engineering, Shahid Bahonar Uni-
versity of Kerman, Kerman, Iran
Department of Computer Engineering, Shahid Bahonar Uni-
versity of Kerman, Kerman, Iran
Department of Computer Engineering, Shahid Bahonar Uni-
versity of Kerman, Kerman, Iran
LEAD_AUTHOR
Mahdi
Eftekhari
m.eftekhari@mail.uk.ac.ir
true
2
Department of Computer Engineering, Shahid Bahonar University
of Kerman, Kerman, Iran
Department of Computer Engineering, Shahid Bahonar University
of Kerman, Kerman, Iran
Department of Computer Engineering, Shahid Bahonar University
of Kerman, Kerman, Iran
AUTHOR
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69
ORIGINAL_ARTICLE
Developing Fuzzy Models for Estimating the Quality of VoIP
This paper presents a novel method for modeling the one-way quality prediction of VoIP, non-intrusively. Intrusive measures of voice quality suffer from common deficiency that is the need of reference signal for evaluating the quality of voice. Owing to this lack, a great deal of effort has been recently devoted for modeling voice quality prediction non-intrusively according to quality degradation parameters, while among the past proposed methods, intelligent techniques have been remarkably successful due to their abilities for modeling the non-linear processes. The present study introduces a procedure for developing fuzzy models, employing Genetic Algorithm (GA) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed method is able to generate optimized fuzzy models in terms of accuracy and complexity. The efficiency of this procedure is compared with and contrasted against 13 regression methods implemented in KEEL as one machine learning tool. Moreover, several experimental results are performed over voice data from 10 different languages. In order to complete the experiment, a comprehensive statistical comparison is also drawn between our proposed method and other previous ones. The results apparently show the efficiency and applicability of this novel method in terms of generating accurate and simple fuzzy models for estimating the VoIP quality.
http://ijfs.usb.ac.ir/article_1395_d41d8cd98f00b204e9800998ecf8427e.pdf
2014-02-25T11:23:20
2018-10-24T11:23:20
49
73
10.22111/ijfs.2014.1395
VoIP
Voice quality
Non-intrusive prediction
PESQ
Neuro-fuzzy
GA
KEEL
F.
Rahdari
rahdarifar@icst.ac.ir
true
1
Computer and Information Technology Department, Institute of Sci-
ence and High Technology and Environmental Sciences, Graduate University of Ad-
vanced Technology, Kerman, Iran
Computer and Information Technology Department, Institute of Sci-
ence and High Technology and Environmental Sciences, Graduate University of Ad-
vanced Technology, Kerman, Iran
Computer and Information Technology Department, Institute of Sci-
ence and High Technology and Environmental Sciences, Graduate University of Ad-
vanced Technology, Kerman, Iran
LEAD_AUTHOR
M.
Eftekhari
m.eftekhari@mail.uk.ac.ir
true
2
Computer Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
Computer Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
Computer Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
AUTHOR
A.
Akbari
akbari@iust.ac.ir
true
3
Computer Engineering Department, Iran University of Science and Tech-
nology, Tehran, Iran
Computer Engineering Department, Iran University of Science and Tech-
nology, Tehran, Iran
Computer Engineering Department, Iran University of Science and Tech-
nology, Tehran, Iran
AUTHOR
M.
Zeinalkhani
zeinalkhani@gmail.com
true
4
Computer Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
Computer Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
Computer Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
AUTHOR
bibitem{Alcala-Fdez:KEEL}
1
J. Alcala-Fdez, L. Sanchez, S. Garcia, M. del Jesus, S. Ventura,
2
J. Garrell, J. Otero, C. Romero, J. Bacardit, V. Rivas, J.
3
Fernandez and F. Herrera, {it KEEL: a software tool to assess
4
evolutionary algorithms for data mining problems}, Journal of
5
Soft Computing - A Fusion of Foundations, Methodologies and
6
Applications, {bf 13}textbf{(3)} (2009), 307-318.
7
bibitem{Andersen:iLBC}
8
S. Andersen and A. Duric, {it Internet low bit rate codec (iLBC),IETF draft}, 2002.
9
bibitem{Beerends:PESQ}
10
J. G. Beerends, A. P. Hekstra, A. W. Rix and M. P. Hollier,
11
{it Perceptual evaluation of speech quality (PESQ): the new ITU
12
standard for end-to-end speech quality assessment part II -
13
psychoacoustic model}, Journal of Audio Eng. Soc., {bf
14
50}textbf{(10)} (2002), 765-778.
15
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ORIGINAL_ARTICLE
Design of an Adaptive Fuzzy Estimator for Force/Position Tracking in Robot Manipulators
This paper presents a stable new algorithm for force/position control in robot manipulators. In this algorithm, position vectors are measured by sensors and then used in the control law. Since using force sensor has some issues such as high costs and technical problems, an approach is presented to overcome these issues. In this respect, force sensor is replaced by an adaptive fuzzy estimator to estimate the external force based on position and velocity measurements. In this approach, force can be properly estimated using universal approximation property of fuzzy systems. Therefore, robots can be controlled in different environments even when no exact mathematical model is available. Since this approach is adaptive, accuracy of the system can be improved with time. Through a theorem the stability of the control system is analyzed using Lyapunov direct method. At last, satisfactory performances of the proposed approach are verified via some numerical simulations and the results are compared with some previous approaches.
http://ijfs.usb.ac.ir/article_1396_5573c6189fbc54601bee32594d6b9478.pdf
2014-02-25T11:23:20
2018-10-24T11:23:20
75
89
10.22111/ijfs.2014.1396
Force/Position control
Adaptive
Fuzzy estimation
Robot manipulator
Stability
Alireza
Naghsh
naghsh a@yahoo.com
true
1
Department of Engineering, Science and Research Branch, Islamic
Azad University, Tehran, Iran
Department of Engineering, Science and Research Branch, Islamic
Azad University, Tehran, Iran
Department of Engineering, Science and Research Branch, Islamic
Azad University, Tehran, Iran
LEAD_AUTHOR
Farid
Sheikholeslam
sheikhsymbol@cc.iut.ac.ir
true
2
Department of Electrical and Computer Engineering, Isfahan
University of Technology, Isfahan, 84156-83111, Iran
Department of Electrical and Computer Engineering, Isfahan
University of Technology, Isfahan, 84156-83111, Iran
Department of Electrical and Computer Engineering, Isfahan
University of Technology, Isfahan, 84156-83111, Iran
AUTHOR
Mohammad
Danesh
danesh@cc.iut.ac.ir
true
3
Department of Mechanical Engineering, Isfahan University of
Technology, Isfahan, 84156-83111, Iran
Department of Mechanical Engineering, Isfahan University of
Technology, Isfahan, 84156-83111, Iran
Department of Mechanical Engineering, Isfahan University of
Technology, Isfahan, 84156-83111, Iran
AUTHOR
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ORIGINAL_ARTICLE
Numerical solution of fuzzy linear Fredholm integro-differential equation by \\fuzzy neural network
In this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and Newton-Cotesmethods with positive coefficient for the solution of linear Fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. Here neural network isconsidered as a part of large field called neural computing orsoft computing. We propose alearning algorithm from the cost function for adjusting fuzzyweights. This paper is one of the first attempts to derive learningalgorithms from fuzzy neural networks with real input, fuzzy output,and fuzzy weights. Finally, we illustrate our approach by numerical examples.
http://ijfs.usb.ac.ir/article_1397_1e44bcaee4c0a281eafa46bae01a2651.pdf
2014-02-25T11:23:20
2018-10-24T11:23:20
91
112
10.22111/ijfs.2014.1397
Fuzzy neural networks
Fuzzy linear Fredholm
integro-differential
Feedforward neural network
Learning algorithm
Maryam
Mosleh
mosleh@iaufb.ac.ir
true
1
Department of Mathematics, Firoozkooh Branch, Islamic Azad Uni-
versity, Firoozkooh, Iran
Department of Mathematics, Firoozkooh Branch, Islamic Azad Uni-
versity, Firoozkooh, Iran
Department of Mathematics, Firoozkooh Branch, Islamic Azad Uni-
versity, Firoozkooh, Iran
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bibitem{oma2} M. Otadi, M. Mosleh and S. Abbasbandy, {it Numerical solution of fully fuzzy linear systems
123
by fuzzy neural network}, Soft Computing, {bf 15} (2011), 1513-1522.
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{it Existence, uniquencess and asymptotic behavior of the solutions
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of a fuzzy differential equation with piecewise constant
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differential eqations}, Information Sciences, {bf 178} (2008), 1756-1779.
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153
ORIGINAL_ARTICLE
Language of General Fuzzy Recognizer
In this note first by considering the notion of general fuzzy automata (for simplicity GFA), we define the notions of direct product, restricted direct product and join of two GFA. Also, we introduce some operations on (Fuzzy) sets and then prove some related theorems. Finally we construct the general fuzzy recognizers and recognizable sets and give the notion of (trim) reversal of a given GFA. In particular, we define the notion of the language of a given general fuzzy $\Sigma$-recognizer and we show that the language of direct product of two $\Sigma$-recognizer is equal to direct product of their languages.
http://ijfs.usb.ac.ir/article_1398_998e413e86612781f35da097555d3bf4.pdf
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113
134
10.22111/ijfs.2014.1398
(Trim) Reversal general fuzzy automata
Active state set
(Coaccessible) Accessible general fuzzy recognizer
Join
Direct product
K.
Abolpour
abolpor kh@yahoo.com
true
1
Department of Mathematics, Kazerun Branch, Islamic Azad Univer-
sity, Kazerun, Iran
Department of Mathematics, Kazerun Branch, Islamic Azad Univer-
sity, Kazerun, Iran
Department of Mathematics, Kazerun Branch, Islamic Azad Univer-
sity, Kazerun, Iran
LEAD_AUTHOR
M. M.
Zahedi
zahedi mm@mail.uk.ac.ir
true
2
Department of Mathematics, Shahid Bahonar University of Kerman,
Kerman, Iran
Department of Mathematics, Shahid Bahonar University of Kerman,
Kerman, Iran
Department of Mathematics, Shahid Bahonar University of Kerman,
Kerman, Iran
AUTHOR
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12
ORIGINAL_ARTICLE
Numerical solutions of fuzzy nonlinear integral equations of the second kind
In this paper, we use the parametric form of fuzzy numbers, and aniterative approach for obtaining approximate solution for a classof fuzzy nonlinear Fredholm integral equations of the second kindis proposed. This paper presents a method based on Newton-Cotesmethods with positive coefficient. Then we obtain approximatesolution of the fuzzy nonlinear integral equations by an iterativeapproach.
http://ijfs.usb.ac.ir/article_1399_b0bad9e9b8effa0799936bdd34f47202.pdf
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135
145
10.22111/ijfs.2014.1399
Fuzzy nonlinear Fredholm integral equations
Newton-Cotes methods
Parametric form of a fuzzy number
M.
Otadi
otadi@iaufb.ac.ir
true
1
Department of Mathematics, Firoozkooh Branch, Islamic Azad Univer-
sity, Firoozkooh, Iran
Department of Mathematics, Firoozkooh Branch, Islamic Azad Univer-
sity, Firoozkooh, Iran
Department of Mathematics, Firoozkooh Branch, Islamic Azad Univer-
sity, Firoozkooh, Iran
LEAD_AUTHOR
M.
Mosleh
mosleh@iaufb.ac.ir
true
2
Department of Mathematics, Firoozkooh Branch, Islamic Azad Univer-
sity, Firoozkooh, Iran
Department of Mathematics, Firoozkooh Branch, Islamic Azad Univer-
sity, Firoozkooh, Iran
Department of Mathematics, Firoozkooh Branch, Islamic Azad Univer-
sity, Firoozkooh, Iran
AUTHOR
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method for solving linear fredholm fuzzy integral equations of
2
the second kind}, Chaos Solitons & Fractals, {bf 31} (2007), 138-146.
3
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4
bibitem{al2} T. Allahviranloo and M. Otadi, {it Gaussian quadratures for approximate of fuzzy multiple integrals}, Applied Mathematics and Computation, {bf 172} (2006), 175-187.
5
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6
New York: Wiley, 1987.
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bibitem{bsa} E. Babolian, H. S. Goghary and S. Abbasbandy, {it Numerical
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solution of linear fredholm fuzzy integral equations of the
9
second kind by Adomian method}, Applied Mathematics and
10
Computation, {bf 161} (2005), 733-744.
11
bibitem{baker} C. T. H. Baker, {it A perspective on the numerical
12
treatment of volterra equations}, Journal of Computational and Appllied Mathematics, {bf 125} (2000), 217-249.
13
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14
Ruiz Galan and M. C. Serrano Perez, {it Biorthogonal systems for solving
15
volterra integral equation systems of the second kind}, Journal of Computational and Appllied Mathematics, {bf 235} (2011), 1875-1883.
16
bibitem{b} A. M. Bica, {it Error estimation in the approximation of
17
the solution of nonlinear fuzzy fredholm integral equations},
18
Information Sciences, {bf 178} (2008), 1279-1292.
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a class of nonlinear fredholm integral equations of the second
21
kind}, Journal of Computational and Applied Mathematics, {bf 232} (2009), 449-454.
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IEEE Trans. System Man Cybernet, {bf 2} (1972), 30-34.
24
bibitem{ct} Y. Chen and T. Tang, {it Spectral methods for weakly
25
singular volterra integral equations with smooth solutions}, Journal of Computational and Appllied Mathematics, {bf 233} (2009), 938-950.
26
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27
number spaces}, Part 1, Fuzzy Sets and Systems, {bf 44} (1991), 33-38.
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30
calculus}, Fuzzy Sets and Systems, {bf 8} (1982), 1-7.
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32
bibitem{fp} M. A. Fariborzi Araghi and N. Parandin, {it Numerical solution of fuzzy fredholm integral equations
33
by the lagrange interpolation based on the extension principle}, Soft Computing, {bf 15} (2011), 2449-2456.
34
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35
of fuzzy differential and integral equations}, Fuzzy Sets and
36
Systems, {bf 106} (1999), 35-48.
37
bibitem{fmk2} M. Friedman, M. Ma and A. Kandel, {it Solution to the fuzzy
38
integral equations with arbitrary kernels}, International Journal of Approximate Reasoning, {bf 20} (1999), 249-262.
39
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40
Sets and Systems, {bf 18} (1986), 31-43.
41
bibitem{h} H. Hochstadt, {it Integral equations}, New York: Wiley,
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43
arithmetic}, Van Nostrand Reinhold, New York, 1985.
44
bibitem{kal}O. Kaleva, {it Fuzzy differential equations}, Fuzzy Sets and
45
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for volterra-fredholm integral equations}, Numerische Math., {bf 56} (1989),
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49
foundations and applications}, Prentice-Hall, 1997.
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51
volterra equations}, SIAM, Philadelphia, PA, 1985.
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53
arithmetic}, Fuzzy Sets and Systems, {bf 108} (1999), 83-90.
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55
bibitem{mo11} M. Mosleh and M. Otadi, {it Numerical solution of fuzzy integral equations using Bernstein polynomials}, Australian Journal of Basic Applied Sciences, {bf 5} (2011), 724-728.
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bibitem{pf1} N. Parandin and M. A. Fariborzi Araghi, {it The approximate solution of linear fuzzy fredholm integral equations of the second kind by using iterative interpolation}, World Academy of Science, Engineering and Technology, {bf 49} (2009), 947-984.
57
bibitem{pf2} N. Parandin and M. A. Fariborzi Araghi, {it The numerical solution of linear fuzzy fredholm integral equations of the second kind by using finite and divided differences methods}, Soft Computing, {bf 15} (2010), 729-741.
58
bibitem{pr} M. L. Puri and D. Ralescu, {it Fuzzy random variables}, Journal of
59
Mathematical Analysis and Applications, {bf 114} (1986), 409-422.
60
bibitem{fard} O. Solaymani Fard and M. Sanchooli, {it Two successive schemes for numerical solution of linear fuzzy fredholm integral equations of the second kind}, Australian Journal of Basic Applied Sciences, {bf 4} (2010), 817-825.
61
bibitem{sy} H. H. Sorkun and S. Yalcinbas, {it Approximate solutions of
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linear volterra integral equation systems with variable
63
coefficients}, Applied Mathematical Modelling, {bf 34} (2010), 3451-3464.
64
bibitem{sb} J. Stoer and R. Bulirsch, {it Introduction to numerical
65
analysis}, Springer-Verlag,New York, 1993.
66
bibitem{laz} L. A. Zadeh, {it The concept of a linguistic variable
67
and its application to approximate reasoning}, Information Sciences, {bf 8} (1975), 199-249.
68
ORIGINAL_ARTICLE
Boundedness and Continuity of Fuzzy Linear Order-Homomorphisms on $I$-Topological\\ Vector Spaces
In this paper, a new definition of bounded fuzzy linear orderhomomorphism on $I$-topological vector spaces is introduced. Thisdefinition differs from the definition of Fang [The continuity offuzzy linear order-homomorphism. J. Fuzzy Math. {\bf5}\textbf{(4)}(1997), 829--838]. We show that the ``boundedness"and `` boundedness on each layer" of fuzzy linear orderhomomorphisms do not imply each other. On the basis,characterizations of continuity of fuzzy linearorder-homomorphisms, and the relation between continuity andboundedness are studied.
http://ijfs.usb.ac.ir/article_1400_24ff2fd4695152cdc861a3e7cec945fe.pdf
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157
10.22111/ijfs.2014.1400
$I$-topological vector spaces
Bounded fuzzy set
Bounded fuzzy
linear order-homomorphism
Jin Xuan
Fang
jxfang@njnu.edu.cn
true
1
School of Mathematical Science, Nanjing Normal University, Nan-
jing, Jiangsu 210023, P. R. China
School of Mathematical Science, Nanjing Normal University, Nan-
jing, Jiangsu 210023, P. R. China
School of Mathematical Science, Nanjing Normal University, Nan-
jing, Jiangsu 210023, P. R. China
LEAD_AUTHOR
Hui
Zhang
zh9907084@sohu.com
true
2
Department of Mathematics, Anhui NormalUniversity, Wuhu, Anhui 241000,
P. R. China
Department of Mathematics, Anhui NormalUniversity, Wuhu, Anhui 241000,
P. R. China
Department of Mathematics, Anhui NormalUniversity, Wuhu, Anhui 241000,
P. R. China
AUTHOR
bibitem{Fang1} J. X. Fang, {it Fuzzy linear order-homomorphism and its
1
structures}, J. Fuzzy Math., {bf 4}textbf{(1)} (1996), 93--102.
2
bibitem{Fang2} J. X. Fang, {it The continuity of fuzzy linear
3
order-homomorphism}, J. Fuzzy Math., {bf 5}textbf{(4)} (1997),
4
bibitem{Fang3} J. X. Fang, {it On local bases of fuzzy topological vector
5
spaces}, Fuzzy Sets and Systems, {bf 87} (1997), 341--347.
6
bibitem{HR} U. H"ohle and S. E. Rodabaugh, eds., {it Mathematics
7
of fuzzy sets: logic, topology, and measure theory}, The Handbooks
8
of Fuzzy Sets Series, Kluwer Academic Publishers,
9
Dordrecht, {bf 3} (1999).
10
bibitem{JY} S. Q. Jiang and C. H. Yan, {it Fuzzy bounded sets and
11
totally fuzzy bounded sets in $I$-topological vector spaces},
12
Iranian Journal of Fuzzy Systems, {bf 6}textbf{(3)} (2009), 73--90.
13
bibitem{KL} A. K. Katsaras and D. B. Liu, {it Fuzzy vector spaces and fuzzy
14
topological vector spaces}, J. Math. Anal. Appl., {bf 58} (1977),
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Sets and Systems, {bf 6} (1981), 85--95.
17
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Sets and Systems, {bf 12} (1984), 143--154.
19
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20
compactness}, J. Math. Anal. Appl., {bf 56} (1976), 621--633.
21
bibitem{PL} P. M. Liu, {it Fuzzy topology I, neighborhood
22
structures of a fuzzy points and Moore-Smith convergence}, J.
23
Math. Anal. Appl., {bf 76} (1980), 571--599.
24
bibitem{Ro1} S. E. Rodabaugh, {it Point-set lattice-theoretic
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topology}, Fuzzy Sets and Systems, {bf 40} (1991), 297--347.
26
bibitem{Ro2} S. E. Rodabaugh, {it Powerset operator based foundation for
27
point-set lattice-theoretic (POSLAT) fuzzy set theories and
28
topologies}, Quaestiones Mathematicae, {bf 20} (1997), 463--530.
29
bibitem{Wang} G. J. Wang, {it Order-homomorphisms of fuzzes}, Fuzzy Sets and
30
Systems, {bf 12} (1984), 281--288.
31
bibitem{Wa} R. H. Warren, {it Neighborhoods, bases and continuity in fuzzy
32
topological spaces}, Rocky Mountain J. Math., {bf 8} (1978),
33
bibitem{WF} C. X. Wu and J. X. Fang, {it Boundedness and locally bounded
34
fuzzy topological vector spaces}, Fuzzy Math. (China), (in Chinese), {bf
35
5}textbf{(4)} (1985), 87--94.
36
bibitem{ZF} H. P. Zhang and J. X. Fang, {it A note on locally bounded
37
$L$-topological vector spaces}, Information Sciences, {bf 179}
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(2009), 1792--1794.
39
ORIGINAL_ARTICLE
Persian-translation vol. 11, no. 1, February 2014
http://ijfs.usb.ac.ir/article_2692_f0227535d3d0e8df719bab08ce5f3bc8.pdf
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161
168
10.22111/ijfs.2014.2692