ORIGINAL_ARTICLE
Cover vol. 13, no. 6, December 2016-
http://ijfs.usb.ac.ir/article_2952_7a1f452ba69b72f793fec1aaf8165e0c.pdf
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10.22111/ijfs.2016.2952
ORIGINAL_ARTICLE
Image Backlight Compensation Using Recurrent Functional Neural Fuzzy Networks Based on Modified Differential Evolution
In this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.The proposed method combines the fuzzy C-means clustering method, a recurrent functional neural fuzzy network (RFNFN), and a modified differential evolution.The proposed RFNFN is based on the two backlight factors that can accurately detect the compensation degree. According to the backlight level, the compensation curve function of a backlight image can be adaptively adjusted. In our experiments, six backlight images are used to verify the performance of proposed method.Experimental results demonstrate that the proposed method performs well in backlight problems.
http://ijfs.usb.ac.ir/article_2819_7fed6e8c834a0fef7dd68ac6cc863235.pdf
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10.22111/ijfs.2016.2819
Neural fuzzy network
Recurrent network
Differential evolution
Fuzzy c-means
Backlight compensation
Contrast enhancement
Sheng-Chih
Yang
true
1
Department of Computer Science and Information Engineering,
National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
Department of Computer Science and Information Engineering,
National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
Department of Computer Science and Information Engineering,
National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
AUTHOR
Cheng-Jian
Lin
cjlin@ncut.edu.tw
true
2
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
LEAD_AUTHOR
Hsueh-Yi
Lin
hyl@ncut.edu.tw
true
3
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
AUTHOR
Jyun-Guo
Wang
jyunguo.wang@gmail.com
true
4
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
AUTHOR
Cheng-Yi
Yu
youjy@ncut.edu.tw
true
5
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC
AUTHOR
[1] C. H. Chen, C. J. Lin and C. T. Lin, A recurrent functional-link-based neural fuzzy system
1
and its applications, Proceedings of the 2007 IEEE Symposium on Computational Intelligence
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in Image and Signal Processing (CIISP 2007), (2007), 415-420.
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[2] J. Duan and G. Qiu, Novel histogram processing for colour image enhancement, Proceedings
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of the Third International Conference on Image and Graphics (ICIG04), Hong Kong, China,
5
(2004), 55-58.
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[3] A. A. Fahmy and A. M. Abdel Ghany, Adaptive functional-based neuro-fuzzy PID incremental
7
controller structure, Neural Computing and Applications, 26(6) (2015), 1423-1438.
8
[4] M. Hojati and S. Gazor, Hybrid adaptive fuzzy identication and control of nonlinear systems,
9
IEEE Transactions on Fuzzy Systems, 10(2) (2002), 198-210.
10
[5] T. H. Huang, K. T. Shih, S. L. Yeh and H. H. Chen, Enhancement of backlight-scaled images,
11
IEEE Transactions on Image Processing, 22(12) (2013), 4587-4597.
12
[6] H. Kabir, A. Al-Wadud and O. Chae, Brightness preserving image contrast enhancement
13
using weighted mixture of global and local transformation functions, The International Arab
14
Journal of Information Technology, 7(4) (2010), 403-410.
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[7] H. Y. Lin, C. Y. Lin, C. J. Lin, S. C. Yang and C. Y. Yu, A study of digital image enlargement
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and enhancement, Mathematical Problems in Engineering, Article ID 825169, (2014).
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[8] D. Menotti, L. Najman, J. Facon and A. A. A. de Araujo, Multi-histogram equalization meth-
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ods for contrast enhancement and brightness preserving, IEEE Transactions on Consumer
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Electronics, 53(3) (2007), 1186-1194.
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[9] A. H. Mohamed, A genetic based neuro-fuzzy controller system, International Journal of
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Computer Applications, 94(1) (2014), 14-17.
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[10] M. Panella and A. S. Gallo, An input-output clustering approach to the synthesis of ANFIS
23
networks, IEEE Transaction on Fuzzy Systems, 13(1) (2005), 69-81.
24
[11] O. Patel, Y. P. S. Maravi and S. Sharma, A comparative study of histogram equalization
25
based image enhancement techniques for brightness preservation and contrast enhancement,
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Signal & Image Processing: An International Journal (SIPIJ), 4(5) (2013), 11-25.
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[12] T. K. S. Paterlini, Dierential evolution and particle swarm optimization in partitional clus-
28
tering, Computational Statics & Data Analysis, 50(5) (2006), 1220-1247.
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from stagnation, Applied Soft Computing, 21(2014), 382V406.
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[16] J. Yen and R. Langari, Fuzzy Logic: intelligence, control, and information, Prentice Hall,
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Taiwan, (2014), 98-102.
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[18] C. Y. Yu, H. Y. Lin, Y. C. Ouyang and T. W. Yu, Modulated AIHT image contrast en-
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hancement algorithm based on contrast-limited adaptive histogram equalization, International
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50
ORIGINAL_ARTICLE
A new attitude coupled with the basic fuzzy thinking to distance between two fuzzy numbers
Fuzzy measures are suitable in analyzing human subjective evaluation processes. Several different strategies have been proposed for distance of fuzzy numbers. The distances introduced for fuzzy numbers can be categorized in two groups:\\1. The crisp distances which explain crisp values for the distance between two fuzzy numbers.\\2. The fuzzy distance which introduce a fuzzy distance for normal fuzzy numbers. It was introduced by Voxman \cite{33} for the first time through using $\alpha$-cut.\\However, both mentioned concepts can lead to unsatisfactory results from the applications point of view, but there is no method, which gives a satisfactory result to all situations. In this paper, a new attitude coupled with fuzzy thinking to the fuzzy distance function on the set of fuzzy numbers is proposed. In this new fuzzy distance, we considered both mentioned attitudes, then we introduced new fuzzy distance based on a combination (hybrid) of those two. Some properties of the proposed fuzzy distance have been discussed. Finally, several examples have been provided to explain the application of the proposed method and compare this methods with others.
http://ijfs.usb.ac.ir/article_2820_d921d86373801123f35a556715b42cba.pdf
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10.22111/ijfs.2016.2820
Pseudo-geometric fuzzy numbers
Transmission average (TA)
Ranking fuzzy numbers
Fuzzy absolute of fuzzy number
Fuzzy distance function (fuzzy metric)
Fazlollah
Abbasi
k_9121946081@yahoo.com
true
1
Department of Mathematics Ayatollah Amoli Branch, Islamic
Azad University, Amol, Iran
Department of Mathematics Ayatollah Amoli Branch, Islamic
Azad University, Amol, Iran
Department of Mathematics Ayatollah Amoli Branch, Islamic
Azad University, Amol, Iran
LEAD_AUTHOR
Tofigh
Allahviranloo
alahviranlo@yahoo.com
true
2
Department of Mathematics, Science and Research Branch,
Islamic Azad University, Tehran, Iran
Department of Mathematics, Science and Research Branch,
Islamic Azad University, Tehran, Iran
Department of Mathematics, Science and Research Branch,
Islamic Azad University, Tehran, Iran
AUTHOR
Saeid
Abbasbandy
abbasbandy@yahoo.com
true
3
Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
[1] S. Abbasbandy and T. Hajjari, A new approach for ranking of trapezoidal fuzzy numbers,
1
Computers and Mathematics with Applications, 57 (2009), 413-419.
2
[2] F. Abbasi, T. Allahviranloo and S. Abbasbandy,A new attitude coupled with fuzzy thinking
3
to fuzzy rings and elds, Journal of Intelligent and Fuzzy Systems, 29 (2015), 851-861.
4
[3] F. Abbasi, T. Allahviranloo and S. Abbasbandy,A new attitude coupled with fuzzy thinking
5
to fuzzy group and subgroup, Journal of Fuzzy Set Valued Analysis, 4 (2015), 1-18.
6
[4] F. Abbasi, T. Allahviranloo and S. Abbasbandy, A new attitude coupled with the basic think-
7
ing to ordering for ranking fuzzy numbers, International Journal of Industrial Mathematics,
8
8(4) (2016), 365-375.
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Journal of Geographical Information Systems, 8 (1994), 271-289.
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[6] M. Ali Beigi, T. Hajjari and E. Ghasem Khani,An Algorithm to Determine Fuzzy Distance
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for fuzzy numbers, Mathematical and Computer Modelling, 43(3-4) (2006), 254-261.
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[9] S. H. Chen and C. C. Wang,Fuzzy distance of trapezoidal fuzzy numbers, In: Proceedings of
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for fuzzy numbers, Mathematical and Computer Modeling, 43( 2006), 254-261, .
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55
ORIGINAL_ARTICLE
A NEW MULTI-OBJECTIVE OPTIMIZATION APPROACH FOR SUSTAINABLE PROJECT PORTFOLIO SELECTION: A REALWORLD APPLICATION UNDER INTERVAL-VALUED FUZZY ENVIRONMENT
Organizations need to evaluate project proposals and select the ones that are the most effective in reaching the strategic goals by considering sustainability issue. In order to enhance the effectiveness and the efficiency of project oriented organizations, in this paper a new multi-objective decision making (MODM) approach of sustainable project portfolio selection is proposed which applies interval-valued fuzzy sets (IVFSs) to consider uncertainty. In the proposed approach, in addition to sustainability criteria, other practical criteria including non-financial benefits, strategic alignment, organizational readiness and project risk are incorporated. The presented approach consists of three main parts: In the first part, a novel composite risk return index based on the IVFSs is introduced and used to form the first model to evaluate the financial return and risk of the proposed projects. In the second part, a new risk reduction compromise ratio model is introduced to evaluate projects versus non-financial criteria. Finally, an MODM model is presented to form the overall objective function of the approach. In order to make the approach more suitable for real-world situations, a group of applicable constraints is included in the proposed approach. The constraints are based on limitations and issues existing in practical project portfolio management. Due to importance of uncertainty and risk in project portfolio selection, they are addressed separately in three parts of the approach. In the first part, a novel downside risk measure is introduced and applied to assess financial risk of projects. In the second part of the approach, not only project risk is accounted for as a criterion, but also a new method is introduced to control and limit the risk of uncertainty and to use the advantages of IVFSs. Finally, the proposed IVF-MODM approach is applied to select the optimal sustainable project portfolio in real case study of a holding company in a developing country. The results show that the approach can successfully address highly uncertain environments. Moreover, risk has been fully explored from different perspectives. Eventually, the approach provided the decision makers with more flexibility in focusing on financial and non-financial criteria in the selection process.
http://ijfs.usb.ac.ir/article_2821_98572c8343f6c2401a8c0c2029fa65ae.pdf
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68
10.22111/ijfs.2016.2821
Sustainable project portfolio selection
Multi-objective optimization
Interval-valued fuzzy sets (IVFSs)
Risks and uncertainties
Holding companies
Vahid
Mohagheghi
true
1
Department of Industrial Engineering, Faculty of Engineering,
Shahed University, Tehran, Iran
Department of Industrial Engineering, Faculty of Engineering,
Shahed University, Tehran, Iran
Department of Industrial Engineering, Faculty of Engineering,
Shahed University, Tehran, Iran
AUTHOR
S. Meysam
Mousavi
true
2
Department of Industrial Engineering, Faculty of Engineering,
Shahed University, Tehran, Iran
Department of Industrial Engineering, Faculty of Engineering,
Shahed University, Tehran, Iran
Department of Industrial Engineering, Faculty of Engineering,
Shahed University, Tehran, Iran
LEAD_AUTHOR
Behnam
Vahdani
b.vahdani@ut.ac.ir
true
3
Faculty of Industrial and Mechanical Engineering, Qazvin Branch,
Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch,
Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch,
Islamic Azad University, Qazvin, Iran
AUTHOR
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method based on interval-valued fuzzy sets, Applied Soft Computing, 9(2) (2009), 457-461.
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robust programming approach for municipal waste-management planning under uncertainty,
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logic, Fuzzy sets and systems, 175(5) (2006), 622-627.
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tion to deal with uncertainty in energy project portfolio selection, assessment and simulation
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folio selection under interval-valued fuzzy environment, Arabian Journal for Science and
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making approach for new product selection in a fuzzy environment, Arabian Journal for
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94
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106
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133
ORIGINAL_ARTICLE
A new approach for solving fuzzy linear Volterra integro-differential equations
In this paper, a fuzzy numerical procedure for solving fuzzy linear Volterra integro-differential equations of the second kind under strong generalized differentiability is designed. Unlike the existing numerical methods, we do not replace the original fuzzy equation by a $2\times 2$ system ofcrisp equations, that is the main difference between our method and other numerical methods.Error analysis and numerical examples are given to show the convergency and efficiency of theproposed method, respectively.
http://ijfs.usb.ac.ir/article_2822_e85f9dade873eaa94511649587a1bf1a.pdf
2016-12-29T11:23:20
2018-10-24T11:23:20
69
87
10.22111/ijfs.2016.2822
Fuzzy number
Fuzzy linear Volterra integro-differential equation
Generalized differentiability
Fuzzy trapezoidal rule
Mojtaba
Ghanbari
mojtaba.ghanbari@gmail.com
true
1
Department of Mathematics, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
Department of Mathematics, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
Department of Mathematics, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
LEAD_AUTHOR
[1] R. Alikhani and F. Bahrami, Global solutions of fuzzy integro-dierential equations under
1
generalized dierentiability by the method of upper and lower solutions, Information Sciences,
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295 (2015), 600{608.
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75(4) (2012), 1810{1821.
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Weierstrass-type, J. Fuzzy Math., 9(3) (2001), 701{708.
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49
ORIGINAL_ARTICLE
Universal Approximation of Interval-valued Fuzzy Systems Based on Interval-valued Implications
It is firstly proved that the multi-input-single-output (MISO) fuzzy systems based on interval-valued $R$- and $S$-implications can approximate any continuous function defined on a compact set to arbitrary accuracy. A formula to compute the lower upper bounds on the number of interval-valued fuzzy sets needed to achieve a pre-specified approximation accuracy for an arbitrary multivariate continuousfunction is then presented. In addition, a method to design the interval-valued fuzzy systems based on $R$- and $S$-implications in order to approximate a given continuousfunction with a required approximation accuracy is represented. Finally, two numerical examples are provided to illustrate the proposed procedure.
http://ijfs.usb.ac.ir/article_2823_cce3164c37f2e5e7a4a00262757ae87b.pdf
2016-12-29T11:23:20
2018-10-24T11:23:20
89
110
10.22111/ijfs.2016.2823
Interval-valued fuzzy sets
Interval-valued fuzzy implications
Interval-valued fuzzy systems
Universal approximator
Sufficient condition
Dechao
Li
true
1
School of Mathematics, Physics and Information Science, Zhejiang Ocean
University, Zhoushan, Zhejiang, 316022, China and Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhoushan, Zhejiang, 316022,
China
School of Mathematics, Physics and Information Science, Zhejiang Ocean
University, Zhoushan, Zhejiang, 316022, China and Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhoushan, Zhejiang, 316022,
China
School of Mathematics, Physics and Information Science, Zhejiang Ocean
University, Zhoushan, Zhejiang, 316022, China and Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhoushan, Zhejiang, 316022,
China
LEAD_AUTHOR
Yongjian
Xie
true
2
College of Mathematics and Information Science, Shaanxi Normal
University, Xi'an, 710062, China
College of Mathematics and Information Science, Shaanxi Normal
University, Xi'an, 710062, China
College of Mathematics and Information Science, Shaanxi Normal
University, Xi'an, 710062, China
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IEEE Transaction on Fuzzy Systems, 12(4) (2004), 524–539
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ORIGINAL_ARTICLE
Stability analysis and feedback control of T-S fuzzy hyperbolic delay model for a class of nonlinear systems with time-varying delay
In this paper, a new T-S fuzzy hyperbolic delay model for a class of nonlinear systems with time-varying delay, is presented to address the problems of stability analysis and feedback control. Fuzzy controller is designed based on the parallel distributed compensation (PDC), and with a new Lyapunov function, delay dependent asymptotic stability conditions of the closed-loop system are derived via linear matrix inequalities (LMIs). Besides, considering the differences between the model and the real system, we extent the model to uncertain T-S fuzzy hyperbolic delay model. Based on the uncertain model, a robust $H_{\infty}$ fuzzy controller is obtained and stability conditions are developed in terms of LMIs. The main advantage of the control based on T-S fuzzy hyperbolic delay model is that it can achieve small control amplitude via ``soft'' constraint approach. Finally, a numerical example and the Van de Vusse example are given to validate the advantages of the proposed method.
http://ijfs.usb.ac.ir/article_2824_a3bec5af1685b15b095fc6509728c28f.pdf
2016-12-29T11:23:20
2018-10-24T11:23:20
111
134
10.22111/ijfs.2016.2824
T-S fuzzy hyperbolic delay model
Small control amplitude
LMIs
robust $H_{infty}$ fuzzy control
Jiaxian
Wang
true
1
School of Mathematics and Statistics, Xidian University, Xi'an, 710071,
China
School of Mathematics and Statistics, Xidian University, Xi'an, 710071,
China
School of Mathematics and Statistics, Xidian University, Xi'an, 710071,
China
AUTHOR
Junmin
Li
jmli@mail.xidian.edu.cn
true
2
School of Mathematics and Statistics, Xidian University, Xi'an, 710071,
China
School of Mathematics and Statistics, Xidian University, Xi'an, 710071,
China
School of Mathematics and Statistics, Xidian University, Xi'an, 710071,
China
LEAD_AUTHOR
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bolic systems, International Journal of Systems Science, 46(9) (2015), 1614{1627.
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Time-Delay, Chinese Control and Decision Conference, (2012), 375{380.
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ORIGINAL_ARTICLE
Some results on $L$-complete lattices
The paper deals with special types of $L$-ordered sets, $L$-fuzzy complete lattices, and fuzzy directed complete posets.First, a theorem for constructing monotone maps is proved, a characterization for monotone maps on an $L$-fuzzy complete lattice is obtained, and it's proved that if $f$ is a monotone map on an $L$-fuzzy complete lattice $(P;e)$, then the least fixpoint of $f$ is meet of a special element of $L^P$. A relation between $L$-fuzzy complete lattices and fixpoints is found and fuzzy versions of monotonicity, rolling, fusion and exchange rules on $L$-complete lattices are stated.Finally, we investigate the set of all monotone maps on a fuzzy directed complete posets, $DCPO$s, andfind a condition which under the set of all fixpoints of a monotone map on a fuzzy $DCPO$ is a fuzzy $DCPO$.
http://ijfs.usb.ac.ir/article_2825_35f830ce3eb516525d4003417026ab00.pdf
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135
152
10.22111/ijfs.2016.2825
Fuzzy complete lattice
Fixpoint
Fuzzy DCPO
Fuzzy directed poset
Monotone map
Anatolij
Dvurecenskij
true
1
Mathematical Institute, Slovak Academy of Sciences, Stefanikova 49, SK-814 73 Bratislava, Slovakia and Depart. Algebra Geom, Palacky Univer.,
17. listopadu 12, CZ-771 46 Olomouc, Czech Republic
Mathematical Institute, Slovak Academy of Sciences, Stefanikova 49, SK-814 73 Bratislava, Slovakia and Depart. Algebra Geom, Palacky Univer.,
17. listopadu 12, CZ-771 46 Olomouc, Czech Republic
Mathematical Institute, Slovak Academy of Sciences, Stefanikova 49, SK-814 73 Bratislava, Slovakia and Depart. Algebra Geom, Palacky Univer.,
17. listopadu 12, CZ-771 46 Olomouc, Czech Republic
AUTHOR
Omid
Zahiri
true
2
University of Applied Science and Technology, Tehran, Iran
University of Applied Science and Technology, Tehran, Iran
University of Applied Science and Technology, Tehran, Iran
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New York, 2002.
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ORIGINAL_ARTICLE
RS-BL-algebras are MV-algebras
We prove that RS-BL-algebras are MV-algebras.
http://ijfs.usb.ac.ir/article_2826_bbdb826f9e20c2a6d18309282069b4cf.pdf
2016-12-29T11:23:20
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153
154
10.22111/ijfs.2016.2826
BL-algebra
MV-algebra
Mathematical fuzzy logic
Esko
Turunen
esko.turunen@tut.fi
true
1
Department of Mathematics, Technical University of Tampere, P.O.
Box 553, 33101, Tampere , Finland
Department of Mathematics, Technical University of Tampere, P.O.
Box 553, 33101, Tampere , Finland
Department of Mathematics, Technical University of Tampere, P.O.
Box 553, 33101, Tampere , Finland
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[1] Hajek, Metamathematics of fuzzy logic, Dordrecht: Kluwer, (1998), 297 pages.
1
[2] Z. Hanikova, On varieties generated by standard BL-algebras, http://www2.cs.cas.
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cz/zuzana/slides/tacl2011-hanikova.pdf.
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s00500-016-2043-z.
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[4] E. Turunen, Mathematics behind Fuzzy Logic, Physica-Verlag, (1999), 191 pages.
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[5] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338-353.
7
ORIGINAL_ARTICLE
Persian-translation vol. 13, no. 6, December 2016
http://ijfs.usb.ac.ir/article_2953_1ef3de757f8c5c9d8ecbba2c228c858f.pdf
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157
164
10.22111/ijfs.2016.2953