ORIGINAL_ARTICLE
Cover vol. 13, no. 6, December 2016-
http://ijfs.usb.ac.ir/article_2952_7a1f452ba69b72f793fec1aaf8165e0c.pdf
2016-12-30T11:23:20
2019-02-16T11:23:20
0
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
2016-12-29T11:23:20
2019-02-16T11:23:20
1
19
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
2
in Image and Signal Processing (CIISP 2007), (2007), 415-420.
3
[2] J. Duan and G. Qiu, Novel histogram processing for colour image enhancement, Proceedings
4
of the Third International Conference on Image and Graphics (ICIG04), Hong Kong, China,
5
(2004), 55-58.
6
[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.
15
[7] H. Y. Lin, C. Y. Lin, C. J. Lin, S. C. Yang and C. Y. Yu, A study of digital image enlargement
16
and enhancement, Mathematical Problems in Engineering, Article ID 825169, (2014).
17
[8] D. Menotti, L. Najman, J. Facon and A. A. A. de Araujo, Multi-histogram equalization meth-
18
ods for contrast enhancement and brightness preserving, IEEE Transactions on Consumer
19
Electronics, 53(3) (2007), 1186-1194.
20
[9] A. H. Mohamed, A genetic based neuro-fuzzy controller system, International Journal of
21
Computer Applications, 94(1) (2014), 14-17.
22
[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,
26
Signal & Image Processing: An International Journal (SIPIJ), 4(5) (2013), 11-25.
27
[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.
29
[13] A. P. Piotrowski, Dierential evolution algorithms applied to neural network training suer
30
from stagnation, Applied Soft Computing, 21(2014), 382V406.
31
[14] R. Storn and K. Price, Dierential evolution-A simple and ecient heuristic for global op-
32
timization over continuous spaces, Journal of Global Optimization, 11(4) (1997), 341-359.
33
[15] M. A.Wadudx, M. H. Kabir, M. A. A. Dewan and O. Chae, A dynamic histogram equalization
34
for image contrast enhancement, IEEE Transactions on Consumer Electronics, 53(2) (2007),
35
[16] J. Yen and R. Langari, Fuzzy Logic: intelligence, control, and information, Prentice Hall,
36
[17] C. Y. Yu, H. Y. Lin and R. N. Lin, Eight-scale image contrast enhancement based on adaptive
37
inverse hyperbolic, International Symposium on Computer, Consumer and Control, Taichung,
38
Taiwan, (2014), 98-102.
39
[18] C. Y. Yu, H. Y. Lin, Y. C. Ouyang and T. W. Yu, Modulated AIHT image contrast en-
40
hancement algorithm based on contrast-limited adaptive histogram equalization, International
41
Journal on Applied Mathematics and Information Sciences, 7(2) (2013), 449-454.
42
[19] C. Y. Yu, Y. C. Ouyang, C. M. Wang and C. I. Chang, Adaptive inverse hyperbolic tan-
43
gent algorithm for dynamic contrast adjustment in displaying scenes, EURASIP Journal on
44
Advances in Signal Processing, 485151 (2010), 1-20.
45
[20] J. Yue, J. Liu, X. Liu and W. Tan, Identication of nonlinear system based on ANFIS with
46
subtractive clustering, The Sixth World Congress on Intelligent Control and Automation
47
(WCICA 2006), 2006, 1852-1856.
48
[21] K. Zuiderveld, Contrast limited adaptive histogram equalization, In: P. Heckbert: Graphics
49
Gems IV, Academic Press 1994
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
2016-12-29T11:23:20
2019-02-16T11:23:20
21
39
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.
9
[5] D. Altman, Fuzzy set theoretic approaches for handling imprecision in spatial analysis, International
10
Journal of Geographical Information Systems, 8 (1994), 271-289.
11
[6] M. Ali Beigi, T. Hajjari and E. Ghasem Khani,An Algorithm to Determine Fuzzy Distance
12
Measure, 13th Iranian Conference on Fuzzy Systems (IFSC), 2013.
13
[7] I. Bloch,On fuzzy distances and their use in image processing under imprecisionm, Pattern
14
Recognition, 32(11) (1999), 1873-1895.
15
[8] C. Chakraborty and D. Chakraborty, Atheoretical development on a fuzzy distance measure
16
for fuzzy numbers, Mathematical and Computer Modelling, 43(3-4) (2006), 254-261.
17
[9] S. H. Chen and C. C. Wang,Fuzzy distance of trapezoidal fuzzy numbers, In: Proceedings of
18
the 9th Joint Conference on Information Sciences, JCIS 2006.
19
[10] C.H. Cheng,A new approach for ranking fuzzy numbers by distance method, Fuzzy Sets and
20
Systems, 95(3) (1998), 307-317.
21
[11] S. H. Chen and C. H. Hsieh,Graded mean integration representation of generalized fuzzy
22
number, Proceeding of TFSA, 1998.
23
[12] C. Chakraborty and D. Chakraborty, A theoretical development on a fuzzy distance measure
24
for fuzzy numbers, Mathematical and Computer Modeling, 43( 2006), 254-261, .
25
[13] T. C. Chu and C. T. Tsao,Ranking fuzzy numbers with an area between the centroid point
26
and original point, Computers Math. Applications, 43 (2002), 111-117.
27
[14] M. M. Deza and E. Deza, Encyclopedia of Distances, 2009.
28
[15] P. D'Urso and P. Giordani, A weighted fuzzy c-means clustering model for fuzzy data, Computational
29
Statistics and Data Analysis, 50(6) (2006), 1496-1523.
30
[16] D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications, 1980.
31
[17] R. Fullor,Fuzzy reasoning and fuzzy optimization, On leave from Department of Operations
32
Research, Eotvos Lorand University, Budapest, 1998.
33
[18] D. Guha and D. Chakraborty,A new approach to fuzzy distance measure and similarity mea-
34
sure between two generalized fuzzy numbers, Applied Soft Computing, 10(1) (2010), 90-99.
35
[19] J. Kacprzyk,Multistage fuzzy control: a prescriptive approach, John Wiley and Sons, Inc,
36
[20] G. J. Klir and B. Yuan,Fuzzy sets and fuzzy logic: theory and applications, Prentice-Hall
37
PTR, Upper Saddlie River, 1995.
38
[21] S. Nezhad, A. Noroozi and A. Makui,Fuzzy distance of triangular fuzzy numbers, Journal of
39
Intelligent and Fuzzy Systems, 2012.
40
[22] A. Rosenfeld,Distance between Fuzzy Sets. Pattern Recognition Letters, 3 (1985), 229-233.
41
[23] H. Rouhparvar, A. Panahi and A. Noorafkan Zanjani,Fuzzy distance measure for fuzzy num-
42
bers, Australian Journal of Basic and Applied Sciences, 5(6) (2011), 258-265.
43
[24] C. Shan-Huo and W. Chien-Chung, Fuzzy distance using fuzzy absolute value, In: Machine
44
Learning and Cybernetics, International Conference, 2009.
45
[25] K. Sridharan and H. E. Stephanou,Fuzzy distance functions for motion planning, In: Tools
46
with Articial Intelligence. TAI '92, Proceedings., Fourth International Conference, 1992.
47
[26] S. R. Sudharsanan, Fuzzy distance approach to routing algorithms for optimal web path
48
estimation, In: Fuzzy Systems, The 10th IEEE International Conference, 2001.
49
[27] L. Stefanini, A generalization of hukuhara dierence and division for interval and fuzzy
50
arithmetic, Fuzzy Sets and Systems, 161 (2010), 1564-1584.
51
[28] L. Tran and L. Duckstein, Comparison of fuzzy numbers using a fuzzy distance measure,
52
Fuzzy Sets and Systems, 130(3) (2002), 331-341.
53
[29] W. Voxman, Some remarks on distances between fuzzy numbers, Fuzzy Sets and Systems,
54
100(1-3) (1998), 353365.
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
2016-12-29T11:23:20
2019-02-16T11:23:20
41
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
[1] B. Ashtiani, F. Haghighirad, A. Makui and A. Montazer, G, Extension of fuzzy TOPSIS
1
method based on interval-valued fuzzy sets, Applied Soft Computing, 9(2) (2009), 457-461.
2
[2] M. Better and F. Glover, Selecting project portfolios by optimizing simulations, The Engineering
3
Economist, 51(2) (2006), 81-97.
4
[3] Y. P. Cai, G. H. Huang, H. W. Lu, Z. F. Yang and Q. Tan, I-VFRP, An interval-valued fuzzy
5
robust programming approach for municipal waste-management planning under uncertainty,
6
Engineering Optimization, 44(5) (2009), 399-418.
7
[4] C. Carlsson, R. Fuller and J. Mezeiz, Project selection with interval-valued fuzzy numbers,
8
In 2011 IEEE 12th International Symposium on Computational Intelligence and Informatics
9
(CINTI) (2011)
10
[5] C. Carlsson, R. Fuller, M. Heikkila and P. Majlender, A fuzzy approach to R&D project
11
portfolio selection, International Journal of Approximate Reasoning, 44(2) (2007), 93-105.
12
[6] C. T. Chen and H. L. Cheng, A comprehensive model for selecting information system project
13
under fuzzy environment, International Journal of Project Management, 27(4) (2009), 389-
14
[7] R. H. Chen, Y. Lin and M. L. Tseng, Multicriteria analysis of sustainable development
15
indicators in the construction minerals industry in China, Resources Policy, 46(1) (2015),
16
[8] N. Chiadamrong, An integrated fuzzy multi-criteria decision making method for manufactur-
17
ing strategies selection, Computers & Industrial Engineering, 37(1) (1999), 433-436.
18
[9] C-Y. Chiu and C. S. Park, Capital budgeting decisions with fuzzy projects, The Engineering
19
Economist 43(2) (1998), 125-150.
20
[10] R. G. Cooper, S. J. Edgett and E. J. Kleinschmidt, New problems, new solutions: making
21
portfolio management more eective, Research-Technology Management, 43(2) (2000), 18-
22
[11] C. Cornelis, G. Deschrijver and E. E. Kerre, Advances and challenges in interval-valued fuzzy
23
logic, Fuzzy sets and systems, 175(5) (2006), 622-627.
24
[12] T. Dyllick and K. Hockerts, Beyond the business case for corporate sustainability, Business
25
strategy and the environment, 11(2) (2002), 130-141.
26
[13] S. Ebrahimnejad, M. H. Hosseinpour and A. M. Nasrabadi, A fuzzy bi-objective mathemati-
27
cal model for optimum portfolio selection by considering in
28
ation rate eects, International
29
Journal of Advanced Manufacturing Technology, 69(1-4) (2013), 595-616.
30
[14] S. Ebrahimnejad, S. M. Mousavi, R. Tavakkoli-Moghaddam, H. Hashemi and B. Vahdani, A
31
novel two-phase group decision making approach for construction project selection in a fuzzy
32
environment, Applied Mathematical Modelling, 36(9) (2012), 4197-4217.
33
[15] J. Elkington, Cannibals with forks: the triple bottom line of 21st century business, Capstone
34
Publishing, Ltd, Oxford, 1997.
35
[16] R. Z. Farahani and N. Asgari, Combination of MCDM and covering techniques in a hierar-
36
chical model for facility location: A case study, European Journal of Operational Research,
37
176(3) (2007), 1839-1858.
38
[17] I. Grattan-Guinness, Fuzzy Membership Mapped onto Intervals and ManyValued Quantities,
39
Mathematical Logic Quarterly, 22(1) (1976), 149-160.
40
[18] L. S. Gaulke, X. Weiyang , A. Scanlon, A. Henck and T. Hinckley, Evaluation Criteria for
41
implementation of a sustainable sanitation and wastewater treatment system at Jiuzhaigou
42
National Park, Sichuan Province, China, Environmental management, 45(1) (2010), 93-104.
43
[19] D. H. Hong and S. Lee, Some algebraic properties and a distance measure for interval-valued
44
fuzzy numbers, Information Sciences, 148(1) (2002), 1-10.
45
[20] L. C. Hsu, A hybrid multiple criteria decision-making model for investment decision making,
46
Journal of Business Economics and Management, 15(3) (2014), 509-529.
47
[21] X. Huang, Mean-semivariance models for fuzzy portfolio selection, Journal of computational
48
and applied mathematics, 217(1) (2008), 1-8.
49
[22] M. J. Hutchins and J. W. Sutherland, An exploration of measures of social sustainability and
50
their application to supply chain decisions, Journal of Cleaner Production, 16(15) (2008),
51
1688-1698.
52
[23] C. L. Hwang and A. S. M. Masud, Multiple objective decision making, methods and applica-
53
tions: a state-of-the-art survey, Vol. 164. Berlin, Springer, 2012.
54
[24] S. Iamratanakul, P. Patanakul and D. Milosevic, Project portfolio selection: From past to
55
present, Proceedings of the 2008 IEEE ICMIT (2008), 287-292.
56
[25] M. G. Kaiser, F. El Arbi and F. Ahlemann, Successful project portfolio management beyond
57
project selection techniques: Understanding the role of structural alignment, International
58
Journal of Project Management, 33(1) (2015), 126-139.
59
[26] K. Khalili-Damghani and S. Sadi-Nezhad, A hybrid fuzzy multiple criteria group decision
60
making approach for sustainable project selection, Applied Soft Computing, 13(1) (2013),
61
[27] K. Khalili-Damghani, S. Sadi-Nezhad, F. H. Lot and M. Tavana, A hybrid fuzzy rule-based
62
multi-criteria framework for sustainable project portfolio selection, Information Sciences, 220
63
(2013), 442-462.
64
[28] D. Kuchta, Fuzzy capital budgeting, Fuzzy Sets and Systems, 11(3) (2000), 367-385.
65
[29] S. H. Liao and S. H. Ho, Investment project valuation based on a fuzzy binomial approach,
66
Information Sciences, 180(11) (2010), 2124-2133.
67
[30] C. Lin and P. J. Hsieh, A fuzzy decision support system for strategic portfolio management,
68
Decision Support Systems, 38(3) (2004), 383-398.
69
[31] H. W. Lu, G. H. Huang and L. He, Development of an interval-valued fuzzy linear-
70
programming method based on innite -cuts for water resources management, Environmental
71
Modelling & Software, 25(3) (2010), 354-361.
72
[32] M. Maleti, D. Maleti, J. J. Dahlgaard, S. M. Dahlgaard-Park and B. Gomiek, Sustainability
73
exploration and sustainability exploitation: from a literature review towards a conceptual
74
framework, Journal of Cleaner Production, 79 (2014), 182-194.
75
[33] K. Manley, Against the odds: small rms in Australia successfully introducing new technology
76
on construction projects, Research Policy, 37(10) (2008), 1751-1764.
77
[34] G. Mavrotas and O. Pechak, Combining mathematical programming and monte carlo simula-
78
tion to deal with uncertainty in energy project portfolio selection, assessment and simulation
79
tools for sustainable energy systems, Springer London, (2013), 333-356.
80
[35] J. Mezei and R. Wikstrom, Aggregation operators and interval-valued fuzzy numbers in deci-
81
sion making, advances in information systems and technologies, Springer Berlin Heidelberg,
82
(2013), 535-544.
83
[36] V. Mohagheghi, S. M. Mousavi and B. Vahdani, A new optimization model for project port-
84
folio selection under interval-valued fuzzy environment, Arabian Journal for Science and
85
Engineering, 40(11) (2015), 3351-3361.
86
[37] S. M. Mousavi, F. Jolai and R. Tavakkoli-Moghaddam, A fuzzy stochastic multi-attribute
87
group decision-making approach for selection problems, Group Decision and Negotiation,
88
22(2) (2013), 207-233.
89
[38] S. M. Mousavi, S. A. Torabi and R. Tavakkoli-Moghaddam, A hierarchical group decision-
90
making approach for new product selection in a fuzzy environment, Arabian Journal for
91
Science and Engineering, 38(11) (2013), 3233-3248.
92
[39] S. M. Mousavi, B. Vahdani, R. Tavakkoli-Moghaddam, S. Ebrahimnejad and M. Amiri, A
93
multi-stage decision making process for multiple attributes analysis under an interval-valued
94
fuzzy environment, International Journal of Advanced Manufacturing Technology, 64 (2013),
95
1263-1273.
96
[40] T. Rashid, I. Beg and S. M. Husnine, Robot selection by using generalized interval-valued
97
fuzzy numbers with TOPSIS, Applied Soft Computing, 21 (2014), 462-468.
98
[41] B. Rebiasz, Fuzziness and randomness in investment project risk appraisal, Computers &
99
Operations Research, 34(1) (2007), 199-210.
100
[42] K. W. Robert, T. M. Parris and A. A. Leiserowitz, What is sustainable development? Goals,
101
indicators, values, and practice, Environment: Science and Policy for Sustainable Development,
102
47 (3) (2005), 8-21.
103
[43] United Nations, Report of the World Commission on Environment and Development: Our
104
Common Future, 1987.
105
[44] B. Vahdani, S. M. Mousavi, R. Tavakkoli-Moghaddam, A. Ghodratnama and M. Mohammadi,
106
Robot selection by a multiple criteria complex proportional assessment method un-
107
der an interval-valued fuzzy environment, International Journal of Advanced Manufacturing
108
Technology, 73(5-8) (2014), 687-697.
109
[45] B. Vahdani, R. Tavakkoli-Moghaddam, S. M. Mousavi and A. Ghodratnama, Soft computing
110
based on new interval-valued fuzzy modied multi-criteria decision-making method, Applied
111
Soft Computing, 13 (2013), 165-172.
112
[46] B. Vahdani, S. M. Mousavi and S. Ebrahimnejad, Soft computing-based preference selection
113
index method for human resource management, Journal of Intelligent and Fuzzy Systems,
114
26(1) (2014), 393-403.
115
[47] J. Wang and W. L. Hwang, A fuzzy set approach for R&D portfolio selection using a real
116
options valuation model, Omega, 35(3) (2007), 247-257.
117
[48] D. J. Watt, B. Kayis and K. Willey, Identifying key factors in the evaluation of tenders for
118
projects and services, International Journal of Project Management, 27(3) (2009), 250-260.
119
[49] J. S. Yao and F. T. Lin, Constructing a fuzzy
120
ow-shop sequencing model based on statistical
121
data, International journal of approximate reasoning, 29(3) (2002), 215-234.
122
[50] K. K. F. Yuen, A hybrid fuzzy quality function deployment framework using cognitive net-
123
work process and aggregative grading clustering: an application to cloud software product
124
development, Neurocomputing, 142 (2014), 95-106.
125
[51] K. K. F. Yuen, Fuzzy cognitive network process: comparisons with fuzzy analytic hierarchy
126
process in new product development strategy, IEEE Transactions on Fuzzy Systems, 22(3)
127
(2014), 597-610.
128
[52] K. K. Yuen and H. C. Lau, A Linguistic Possibility-Probability Aggregation Model for decision
129
analysis with imperfect knowledge, Applied Soft Computing, 9(2) (2009), 575-589.
130
[53] W. G. Zhang, Q. Mei, Q. Lu and W. L. Xiao, Evaluating methods of investment project
131
and optimizing models of portfolio selection in fuzzy uncertainty, Computers & Industrial
132
Engineering, 61(3) (2011), 721-728.
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
2019-02-16T11:23:20
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,
2
295 (2015), 600{608.
3
[2] R. Alikhani, F. Bahrami and A. Jabbari, Existence of global solutions to nonlinear fuzzy
4
Volterra integro-dierential equations, Nonlinear Analysis: Theory, Methods & Applications,
5
75(4) (2012), 1810{1821.
6
[3] G. A. Anastassiou and S. G. Gal, On a fuzzy trigonometric approximation theorem of
7
Weierstrass-type, J. Fuzzy Math., 9(3) (2001), 701{708.
8
[4] K. Balachandran and P. Prakash, On fuzzy volterra integral equations with deviting argu-
9
ments, Journal of Applied Mathematics and Stochastic Analysis, 2 (2004), 169{176.
10
[5] B. Bede and S. G. Gal, Almost Periodic fuzzy-number valued functions, Fuzzy Sets and
11
Systems, 147 (2004), 385{403.
12
[6] B. Bede and S. G. Gal, Generalizations of dierentiablity of fuzzy number valued function
13
with application to fuzzy dierential equations, Fuzzy Sets and Systems, 151 (2005), 581{599.
14
[7] B. Bede and S. G. Gal, Quadrature rules for integrals of fuzzy-number-valued functions, Fuzzy
15
Sets and Systems, 145 (2004), 359{380.
16
[8] Y. Chalco-Cano and H. Roman-Flores, On new solutions of fuzzy dierential equations,
17
Chaos, Solitons and Fractals, 38 (2008), 112{119.
18
[9] D. Dubois and H. Prade, Towards fuzzy dierential calculus. I. Integration of fuzzy mappings,
19
Fuzzy Sets and Systems, 8(1) (1982), 1{17.
20
[10] D. Dubois and H. Prade, Towards fuzzy dierential calculus. II. Integration on fuzzy intervals,
21
Fuzzy Sets and Systems, 8(2) (1982), 105{116.
22
[11] D. Dubois and H. Prade, Towards fuzzy dierential calculus: III, dierentiation, Fuzzy Sets
23
and Systems, 8 (1982), 225{233.
24
[12] S. G. Gal, Approximation theory in fuzzy setting, in: G.A. Anastassiou (Ed.), Handbook
25
of Analytic-Computational Methods in Applied Mathematics, Chapman Hall, CRC Press,
26
(2000), 617{666.
27
[13] M. Ghatee and S. M. Hashemi, Ranking function-based solutions of fully fuzzied minimal
28
ow problem, Information Sciences, 177 (2007), 4271{4294.
29
[14] R. Goetschel and W. Voxman, Elementary fuzzy calculus, Fuzzy sets and Systems, 18 (1986),
30
[15] S. Hajighasemi, T. Allahviranloo, M. Khezerloo, M. Khorasany and S. Salahshour, Existence
31
and uniqueness of solutions of fuzzy volterra integro-dierential equations, IPMU, Part II,
32
CCIS 81, (2010), 491{500.
33
[16] O. Kaleva, Fuzzy dierential equations, Fuzzy Sets and Systems, 24 (1987), 301{317.
34
[17] J. Y. Park and H. K. Han, Existence and uniqueness theorem for a solution of fuzzy Volterra
35
integral equations, Fuzzy Sets and Systems, 105 (1999), 481{488.
36
[18] J. Y. Park and J. U. Jeong, A note on fuzzy integral equations, Fuzzy Sets and Systems, 108
37
(1999), 193{200.
38
[19] M. L. Puri and D. Ralescu, Dierentials of fuzzy functions, J. Math. Anal. Appl., 91 (1983),
39
[20] M. L. Puri and D. Ralescu, Fuzzy random variables, J. Math. Anal. Appl., 114 (1986),
40
[21] A. Ralston and P. Rabinowitz, First course in numerical analysis, McGraw-Hill, 1978.
41
[22] S. Salahshour and T. Allahviranloo, Application of fuzzy dierential transform method for
42
solving fuzzy Volterra integral equations, Applied Mathematical Modelling, 37(3) (2013),
43
1016{1027.
44
[23] S. Seikkala, On the fuzzy initial value problem, Fuzzy Sets and Systems, 24(3) (1987), 319{
45
[24] S. J. Song, Q.Y. Liu and Q. C Xu, Existence and comparison theorems to Volterra fuzzy
46
integral equation in (En;D), Fuzzy Sets and Systems, 104 (1999), 315{321.
47
[25] C. Wu and Z. Gong, On Henstock integral of fuzzy-number-valued functions I, Fuzzy Sets
48
and Systems, 120 (2001), 523{532.
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
2019-02-16T11:23:20
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
AUTHOR
[1] O. Castillo and P. Melin, A review on interval type-2 fuzzy logic applications in intelligent
1
control, Information Sciences, 279 (2014), 615–631.
2
[2] C. Cornelis, G. Deschrijver and E. E. Kerre, Implication in intuitionistic and interval-valued
3
fuzzy set theory: construction, classification and application, International Journal of Approximate
4
Reasoning, 35 (2004), 55–95.
5
[3] S. Coupland and R. John, A fast geometric method for defuzzification of type-2 fuzzy sets,
6
IEEE Transaction on Fuzzy Systems, 16(4) (2008), 929–941.
7
[4] T. Dereli, A. Baykasoglu, K. Altun, A. Durmusoglu and I. B. T¨urksen, Industrial applications
8
of type-2 fuzzy sets and systems: a concise review, Computer in Industry, 62 (2011), 125–137.
9
[5] G. Deschrijver, C. Cornelis and E. E. Kerre, On the representation of intuitionistic fuzzy
10
t-norms and t-conorms, IEEE Transaction on Fuzzy Systems, 12(1) (2004), 45–61.
11
[6] G. Deschrijver and E. E. Kerre, Classes of intuitionistic fuzzy t-norms satisfying the residuation
12
principle, International Journal of Uncertainty Fuzziness Knowledge-Based Systems,
13
11(6) (2003), 691–709.
14
[7] G. Deschrijver and E. E. Kerre, On the relationship between some extensions of fuzzy set
15
theory, Fuzzy Sets and Systems, 133 (2003), 227–235.
16
[8] A. Doostparast, M. H. Fazel Zarandi and H. Zakeri, On type-reduction of type-2 fuzzy sets:
17
A review, Applied Soft Computing, 27 (2015), 614–627.
18
[9] D. Dubois, On ignorance and contradiction considered as truth-values, Logic Journal of the
19
IGPL, 16(2) (2008), 195–216.
20
[10] B. V. Gasse, C. Cornelis, G. Deschrijver and E. E. Kerre, Triangle algebras: A formal logic
21
approach to interval-valued residuated lattices, Fuzzy Sets and Systems, 159 (2008), 1042–
22
[11] M. B. Gorza lczany, A method of inference in approximate reasoning based on interval-valued
23
fuzzy sets, Fuzzy Sets and Systems, 21(1) (1987), 1–17.
24
[12] M. B. Gorza lczany, An interval-valued fuzzy inference method-Some basic properties, Fuzzy
25
Sets and Systems, 31(2) (1989), 243–251.
26
[13] S. Greenfield, F. Chiclana, R. I. John and S. Coupland, The sampling method of defuzzification
27
for type-2 fuzzy sets: experimental evaluation, Information Sciences, 189 (2012),
28
77–92.
29
[14] H. Hagras, A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,
30
IEEE Transaction on Fuzzy Systems, 12(4) (2004), 524–539
31
[15] H. R. Hassanzadeh, M. T. A. Akbarzadeh and A. Rezaei, An interval-valued fuzzy controller
32
for complex dynamical systems with application to a 3-PSP parallel robot, Fuzzy Sets and
33
Systems, 235(16) (2014), 83–100.
34
[16] M. Y. Hsiao, T. S. Li, J. Z. Lee, C. H. Chao and S. H. Tsai, Design of interval type-2 fuzzy
35
sliding-mode controller, Information Sciences, 178(6) (2008), 1696–1716.
36
[17] C. F. Juang and Y. W. Tsao, A type-2 self-organizing neural fuzzy system and its FPGA implementation,
37
IEEE Transaction on System Man Cybernet. Part B: Cybernet, 38(6) (2008),
38
1537–1548.
39
[18] H. K. Lam, H. Li, C. Deters, E. L. Secco, H. A. Wurdemann and K. Althoefer, Control design
40
for interval type-2 fuzzy systems under imperfect premise matching, IEEE Transactions on
41
Industrial Electronics, 61(2) (2014), 956–968, art. no. 6480840.
42
[19] D. C. Li, Y. M. Li and Y. J. Xie, Robustness of interval-valued fuzzy inference, Information
43
Science, 181 (2011), 4754–4764.
44
[20] Y. M. Li and Y. J. Du, Indirect adaptive fuzzy observer and controller design based on interval
45
type-2 T-S fuzzy model, Applied Mathematical Modelling, 36(4) (2012), 1558–1569.
46
[21] Y. M. Li, Z. K. Shi and Z. H. Li, Approximation theory of fuzzy systems based upon genuine
47
many-valued implications: SISO cases, Fuzzy Sets and Systems, 130 (2002), 147–157.
48
[22] Y. M. Li, Z. K. Shi and Z. H. Li, Approximation theory of fuzzy systems based upon genuine
49
many-valued implications: MIMO cases, Fuzzy Sets and Systems, 130 (2002), 159–174.
50
[23] Q. Liang and J. M. Mendel, Interval type-2 fuzzy logic systems: theory and design, IEEE
51
Transaction on Fuzzy Systems, 8 (2000), 535–550.
52
[24] O. Linda and M. Manic, Uncertainty-robust design of interval type-2 fuzzy logic controller
53
for delta parallel robot, IEEE Trans. Ind. Inf. 7(4) (2011), 661–670.
54
[25] X. Liu and J. Mendel, Connect Karnik-Mendel algorithms to root-finding for computing the
55
centroid of an interval type-2 fuzzy set, IEEE Transaction on Fuzzy Systems, 19(4) (2011),
56
652–665.
57
[26] S. Mandal and B. Jayaram, SISO fuzzy relational inference systems based on fuzzy implications
58
are universal approximators, Fuzzy Sets and Systems, 277 (2015), 1–21.
59
[27] V. Nov´ak and S. Lehmke, Logical structure of fuzzy IF-THEN rules, Fuzzy Sets and Systems,
60
157(15) (2006), 2003–2029.
61
[28] V. Nov´ak, I. Perfilieva and J. Mˇckˇcr, Mathematical Principles of Fuzzy Logic, Kluwer Academic
62
Publishers, Boston, 1999.
63
[29] I. Perfilieva, Normal forms in BL-algebra off unctions and their contribution to universal
64
approximation, Fuzzy Sets and Systems, 143(1) (2004), 111–127.
65
[30] I. Perfilieva and V. Kreinovich, A new universal approximation result for fuzzy systems,
66
which reflects CNF-DNF duality, Int. J. Intell. Syst. 17(12) (2002), 1121–1130.
67
[31] Y. M. Tang and X. P. Liu, Differently implicational universal triple I method of (1, 2, 2)
68
type, Computers and Mathematics with Applications, 59(6) (2010), 1965–1984.
69
[32] I. B. T¨urksen, Type 2 representation and reasoning for CWW, Fuzzy Sets and Systems, 127
70
(2002), 17–36.
71
[33] I. B. T¨urksen and Y. Tian, Interval-valued fuzzy sets representation on multiple antecedent
72
fuzzy S-implications and reasoning, Fuzzy Sets and Systems, 52(2) (1992), 143–167.
73
[34] G. Wang and X. Li, Correlation and information energy of interval-valued fuzzy numbers,
74
Fuzzy Sets and Systtem, 103(1) (1999), 169–175.
75
[35] D. Wu, On the fundamental differences between interval type-2 and type-1 fuzzy logic controllers,
76
IEEE Transactions on Fuzzy Systems, art. no. 6145645, 20(5) (2012), 832–848.
77
[36] D. Wu and W. W. Tan, A type-2 fuzzy logic controller for the liquid-level process, in: 2004
78
IEEE International Conference on Fuzzy Systems, (2004), Proceedings. 2 (2004), 953–958.
79
[37] H. Ying, Sufficient conditions on general fuzzy systems as function approximators, Automatic,
80
30(3) (1994), 521–525.
81
[38] H. Ying, General interval type-2 Mamdani fuzzy systems are universal approximators, Proceedings
82
of North American Fuzzy Information Processing Society Conference, New York,
83
NY, May 19–22, 2008.
84
[39] H. Ying, Interval type-2 Takagi-Sugeno fuzzy systems with linear rule consequent are universal
85
approximators, The 28th North American Fuzzy Information Processing Society Annual
86
Conference, Cincinnati, Ohio, June 14–17, 2009.
87
[40] L. A. Zadeh, The concepts of a linguistic variable and its application to approximate reasoning
88
(I), (II), Information Science, 8 (1975), 199–249; 301–357.
89
[41] L. A. Zadeh, The concepts of a linguistic variable and its application to approximate reasoning
90
(III), Information Science, 9 (1975), 43–80.
91
[42] W. Y. Zeng and S. Feng, Approximate reasoning algorithm of interval-valued fuzzy sets based
92
on least square method, Information Sciences, 272 (2014), 73–83.
93
[43] H. Zhou and H. Ying, A method for deriving the analytical structure of a broad class of
94
typical interval type-2 mamdani fuzzy controllers, in: IEEE Transactions on Fuzzy Systems,
95
art. no. 6341818, 21(3) (2013), 447–458.
96
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
2019-02-16T11:23:20
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
[1] P. Balasubramaniam and V. M. Revathi, H1 Filtering for Markovian switching system with
1
mode-dependent time-varying delays, Circuits Systems and Signal Processing, 33(2) (2014),
2
[2] P. Balasubramaniam and T. Senthilkumar, Delay-dependent robust stabilization and H1
3
control for uncertain stochastic T-S fuzzy systems with multiple time delays, Iranian Journal
4
of Fuzzy Systems, 9(2) (2012), 89{111.
5
[3] Y. Y. Cao and P. M. Frank, Stability analysis and synthesis of nonlinear time-delay systems
6
via linear Takagi-Sugeno fuzzy models, Fuzzy sets and systems, 124(2) (2001), 213{229.
7
[4] M. L. Chen and J. M. Li, Modeling and control of T-S fuzzy hyperbolic model for a class of
8
nonlinear systems, Proceedings of International Conference on Modelling, Identication and
9
Control, (2012), 57{62.
10
[5] M. L. Chen and J. M. Li, Non-fragile guaranteed cost control for Takagi-Sugeno fuzzy hyper-
11
bolic systems, International Journal of Systems Science, 46(9) (2015), 1614{1627.
12
[6] T. H. Chen, C. C. Kung and K. H. Su, The piecewise Lyapunov functions based the delay-
13
independent H1 controller design for a class of time-delay T-S fuzzy system, IEEE Interna-
14
tional Conference on Systems, Man and Cybernetics, (2007), 121{126.
15
[7] C. S. Chiu, W. T. Yang and T. S. Chiang, Robust output feedback control of T-S fuzzy time-
16
delay systems, IEEE Symposium on Computational Intelligence in Control and Automation,
17
(2013), 45{50.
18
[8] G. Feng, A survey on analysis and design of model-based fuzzy control systems, IEEE Trans-
19
actions on Fuzzy Systems, 14(5) (2006), 676{697.
20
[9] Daniel W. C. Ho and Y. Niu, Robust fuzzy design for nonlinear uncertain stochastic systems
21
via sliding-mode control, IEEE Transactions on Fuzzy Systems, 15(3) (2007), 350{358.
22
[10] Z. Hong and Z. F. Li, Stabilization for a class of T-S uncertain nonlinear systems with
23
Time-Delay, Chinese Control and Decision Conference, (2012), 375{380.
24
[11] M. Y. Hsiao, C. H. Liu, S. H. Tsai and et al, A Takagi-Sugeno fuzzy-model-based modeling
25
method, IEEE International Conference on Fuzzy Systems, (2010), 1{6.
26
[12] J. M. Li, G. Zhang, Non-fragile guaranteed cost control of T-S fuzzy time varying delay
27
systems with local bilinear models, Iranian Journal of Fuzzy Systems, 9(2) (2012), 43{62.
28
[13] Y. M. Li and S. C. Tong, Prescribed performance adaptive fuzzy output-feedback dynamic
29
surface control for nonlinear large-scale systems with time delays, Information Sciences, 292
30
(2015), 125{142.
31
[14] C. H. Lien and K. W. Yu, Robust control for Takagi-Sugeno fuzzy systems with time-varying
32
state and input delays, Chaos, Solitons and Fractals, 35(5) (2008), 1003{1008.
33
[15] C. Lin, Q. G. Wang and T. H. Lee, Delay-dependent LMI conditions for stability and stabi-
34
lization of T-S fuzzy systems with bounded time-delay, Fuzzy Sets Systems, 157(9) (2006),
35
1229{1247.
36
[16] C. Lin, Q. G. Wang, T. H. Lee and Y. He, Fuzzy weighting-dependent approach to H1 lter
37
design for time-delay fuzzy systems, IEEE Transactions on Signal Processing, 55(6) (2007),
38
2746{2751.
39
[17] T. Takagi and M. Sugeno, Fuzzy identication of systems and its applications to modelling
40
and control, IEEE Transactions on Systems, Man and Cybernetics, 15(1) (1985), 116{132.
41
[18] K. Tanaka, T. Ikeda and H. O. Wang, Fuzzy regulators and fuzzy observers: relaxed stability
42
conditions and LMI-based designs, IEEE Transactions on Fuzzy Systems, 6(2) (1998), 250{
43
[19] K. Tanaka and H. O. Wang, Fuzzy control systems design and analysis: A linear matrix
44
inequality approach, John Wiley and Sons, 2002.
45
[20] S. H. Tsai and C. J. Fang, A novel relaxed stabilization condition for a class of T-S time-delay
46
fuzzy systems, IEEE International Conference on Fuzzy Systems, (2014), 2294{2299.
47
[21] C. S. Tseng, B. S. Chen and H. J. Uang, Fuzzy tracking control design for nonlinear dynamic
48
systems via T-S fuzzy model, IEEE Transactions on Fuzzy Systems, 9(3)(2001), 381{392.
49
[22] G. Wang, Y. Wang and D. S. Yang, New sucient conditions for delay-dependent robust
50
H1 control of uncertain nonlinear system based on fuzzy hyperbolic model with time-varying
51
delays, Chinese Control and Decision Conference, (2012), 1138{1143.
52
[23] S. B. Wang, Y. Y. Wang and L. K. Zhang, Time-delay dependent state feedback fuzzy-
53
predictive control of time-delay T-S fuzzy model, Fifth International Conference on Fuzzy
54
Systems and Knowledge Discovery, (2008), 129{133.
55
[24] T. T. Wang, H. C. Yan, H. B. Shi and H. Zhang, Event-triggered H1 control for networked
56
T-S fuzzy systems with time delay, IEEE International Conference on Information and Au-
57
tomation, (2014), 194{199.
58
[25] Y. Y. Wang, H. G. Zhang, J. Y. Zhang and et al, An sos-based observer design for discrete-
59
time polynomial fuzzy systems, International Journal of Fuzzy Systems, 17(1) (2015), 94{104.
60
[26] G. L. Wei, G. Feng and Z. D. Wang, Robust H1 control for discrete-time fuzzy systems with
61
innite-distributed delays, IEEE Transactions on Fuzzy Systems, 17(1) (2009), 224{232.
62
[27] H. N. Wu and H. X. Li, New approach to delay dependent stability analysis and stabilization
63
for continuous-time fuzzy systems with time-varying delay, IEEE Transactions on Fuzzy
64
Systems, 15(3) (2007), 482{493.
65
[28] H. G. Zhang, Fuzzy hyperbolic model - modeling, control and application, Beijing: Science
66
Press, 2009.
67
[29] H. G. Zhang, Q. X. Gong and Y. C. Wang, Delay-dependent robust H1 control for uncertain
68
fuzzy hyperbolic systems with multiple delays, Progress in Natural Science, 18(1) (2008),
69
[30] H. G. Zhang, X. R. Liu, Q. X. Gong and et al, New sucient conditions for robust H1 fuzzy
70
hyperbolic tangent control of uncertain nonlinear systems with time-varying delay, Fuzzy Sets
71
and Systems, 161(15) (2010), 1993{2011.
72
[31] H. G. Zhang, S. X. Lun and D. R. Liu, Fuzzy H1 lter design for a class of nonlinear discrete-
73
time systems with multiple time delays, IEEE Transactions on Fuzzy Systems, 15(3) (2007),
74
[32] H. G. Zhang and Y. B. Quan, Modeling, identication and control of a class of nonlinear
75
system, IEEE Transactions on Fuzzy Systems, 9(2) (2001), 349{354.
76
[33] H. G. Zhang and X. P. Xie, Relaxed Stability Conditions for Continuous-Time T-S Fuzzy-
77
Control Systems Via Augmented Multi-Indexed Matrix Approach, IEEE Transactions on
78
Fuzzy Systems, 19(3) (2011), 478{492.
79
[34] H. G. Zhang, J. L. Zhang, G. H. Yang and et al, Leader-based optimal coordination con-
80
trol for the consensus problem of multiagent dierential games via fuzzy adaptive dynamic
81
programming, IEEE Transactions on Fuzzy Systems, 23(1) (2015), 152{163.
82
[35] J. H. Zhang, P. Shi and J. Q. Qiu, Non-fragile guaranteed cost control for uncertain stochastic
83
nonlinear time-delay systems, Journal of the Franklin Institute, 346(7) (2009), 676{690.
84
[36] Z. Y. Zhang, C. Lin and B. Chen, New stability and stabilization conditions for T-S fuzzy
85
systems with time delay, Fuzzy Sets and Systems, 263(C) (2015), 82{91.
86
[37] Y. Zhao and H. J. Gao, Fuzzy-model-based control of an overhead crane with input delay and
87
actuator saturation, IEEE Transactions on Fuzzy Systems, 20(1) (2012), 181{186.
88
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
2016-12-29T11:23:20
2019-02-16T11:23:20
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
LEAD_AUTHOR
[1] R. Belohlavek, Fuzzy relational systems: foundations and principles, Kluwer Acad. Publ.,
1
New York, 2002.
2
[2] R. Belohlavek, Concept lattices and order in fuzzy logic, Ann. Pure Appl. Logic, 128 (2004),
3
[3] T. S. Blyth, Lattices and ordered algebraic structures, Springer-Verlag, London, 2005
4
[4] U. Bodenhofer, A similarity-based generalization of fuzzy orderings preserving the classical
5
axioms, Internat. J. Uncertain. Fuzziness Knowledge-Based Systems, 8(5) (2000), 593{610.
6
[5] U. Bodenhofer, Representations and constructions of similarity-based fuzzy orderings, Fuzzy
7
Sets and Systems, 137(1) (2003), 113{136.
8
[6] R. A. Borzooei, A. Dvurecenskij and O. Zahiri, L-Ordered and L-lattice ordered groups,
9
Information Sciences, 314 (2015), 118{134.
10
[7] B. A. Davey and H. A. Priestley, Introduction to lattices and order, Cambridge University
11
Press, Second edition, 2002.
12
[8] A. Davis, A characterization of complete lattices, Pacic J. Math., 5 (1955), 311{319.
13
[9] M. Demirci, A theory of vague lattices based on many-valued equivalence relations - I: general
14
representation results, Fuzzy Sets and Systems, 151(3) (2005), 437{472.
15
[10] M. Demirci. A theory of vague lattices based on many-valued equivalence relations - II: com-
16
plete lattices, Fuzzy Sets and Systems, 151(3) (2005), 473{489.
17
[11] L. Fan, A new approach to quantitative domain theory, Electronic Notes Theor. Comp. Sci.,
18
45 (2001), 77{87.
19
[12] R. Fuentes-Gonzalez, Down and up operators associated to fuzzy relations and t-norms: A
20
denition of fuzzy semi-ideals, Fuzzy Sets and Systems, 117 (2001), 377{389.
21
[13] P. T. Johnstone, `Stone Spaces, Cambridge University Press, Cambridge, 1982.
22
[14] H. Lai and D. Zhang, Complete and directed complete
23
-categories, Theor. Computer Sci.,
24
388 (2007), 1{25.
25
[15] S. Su and Q. Li, Algebraic fuzzy directed-complete posets, Neural Comput. & Applic., 21(1)
26
(2012), 255{265.
27
[16] A. Tarski, A lattice theoretical xed point theorem and its applications, Pacic J. Math., 5
28
(1955), 285{309.
29
[17] L. Valverde, On the structure of F-indistinguishability operators, Fuzzy Sets and Systems,
30
17(3) (1985), 313{328.
31
[18] K. R. Wagner, Solving recursive domain equations with enriched categories, Ph.D. Thesis,
32
Carnegie Mellon University, Technical Report CMU-CS-94-159, July 1994.
33
[19] W. Yao, An approach to fuzzy frames via fuzzy posets, Fuzzy Sets and Systems, 166 (2011),
34
[20] W. Yao and L. X. Lu, Fuzzy Galois connections on fuzzy posets, Math. Log. Quart., 55
35
(2009), 105{112.
36
[21] W. Yao, Quantitative domains via fuzzy sets: Part I: Continuity of fuzzy directed complete
37
posets, Fuzzy Sets and Systems, 161 (2010), 973{987.
38
[22] W. Yao and F. G. Shi, Quantitative domains via fuzzy sets: Part II: Fuzzy Scott topology on
39
fuzzy directed-complete posets, Fuzzy Sets and Systems, 173 (2011), 60{80.
40
[23] Q. Y. Zhang and L. Fan, Continuity in quantitative domains, Fuzzy Sets and Systems, 154
41
(2005), 118{131.
42
[24] Q. Y. Zhang, W. X. Xie and L. Fan, Fuzzy complete lattices, Fuzzy Sets and Systems, 160
43
(2009), 2275{2291.
44
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
2019-02-16T11:23:20
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
LEAD_AUTHOR
[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.
2
cz/zuzana/slides/tacl2011-hanikova.pdf.
3
[3] S. Motamed and L. Torkzadeh, A new class of BL-algebras, Soft Computing, doi: 10.1007/
4
s00500-016-2043-z.
5
[4] E. Turunen, Mathematics behind Fuzzy Logic, Physica-Verlag, (1999), 191 pages.
6
[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
2016-12-30T11:23:20
2019-02-16T11:23:20
157
164
10.22111/ijfs.2016.2953