ORIGINAL_ARTICLE
Cover vol.2, no.2 October 2005
http://ijfs.usb.ac.ir/article_3123_22bd51a9452559d8810ff5b8a8a8c882.pdf
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10.22111/ijfs.2005.3123
ORIGINAL_ARTICLE
INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES
The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC neural networks are the supervised neural networkswhich use the fuzzified versions of Learning Vector Quantization (LVQ). In this paper,several important adaptive learning algorithms are compared from the viewpoint of structure andlearning rule. The performances of several adaptive learning algorithms are compared usingIris data set.
http://ijfs.usb.ac.ir/article_477_b026c6b686fee4da511735fefc3be005.pdf
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10.22111/ijfs.2005.477
Neural Networks
Fuzzy logic
Fuzzy neural networks
Learning rule
Fuzzification
YONG SOO
KIM
kystj@dju.ac.kr
true
1
DIVISION OF COMPUTER ENGINEERING, DAEJEON UNIVERSITY, DAEJEON, 300-716,
KOREA
DIVISION OF COMPUTER ENGINEERING, DAEJEON UNIVERSITY, DAEJEON, 300-716,
KOREA
DIVISION OF COMPUTER ENGINEERING, DAEJEON UNIVERSITY, DAEJEON, 300-716,
KOREA
LEAD_AUTHOR
Z.
ZENN BIEN
zbien@ee.kaist.ac.kr
true
2
DEPARTMENT OF ELECRICAL ENGINEERING AND COMPUTER SCIENCE, KAIST,
DAEJEON, 305-701, KOREA
DEPARTMENT OF ELECRICAL ENGINEERING AND COMPUTER SCIENCE, KAIST,
DAEJEON, 305-701, KOREA
DEPARTMENT OF ELECRICAL ENGINEERING AND COMPUTER SCIENCE, KAIST,
DAEJEON, 305-701, KOREA
AUTHOR
[1] J. C. Bezdek, E. C. Tsao and N. R. Pal, Fuzzy Kohonen clustering networks, Proceeding of the First
1
IEEE Conference on Fuzzy System, (1992) 1035-1043.
2
[2] J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York,
3
[3] G. A. Carpenter and S. Grossberg, A massively parallel architecture for a self-organization neural
4
pattern recognition machine, Computer vision, Graphics, and Image processing, 37 (1987) 54-115.
5
[4] G. A. Capenter, S. Grossberg and D. B. Rosen, Fuzzy ART : fast stable learning and categorization
6
of analog pattern by an adaptive resonance systems, Neural Networks, 4 (1992) 759-772.
7
[5] F-L Chung and T. Lee, A fuzzy learning model for membership function estimation and pattern
8
classification, Proceedings of the third IEEE conference on Fuzzy systems, 1 (1994) 426-431.
9
[6] F. L. Chung and T. Lee, Fuzzy competitive learning, Neural Networks, 7 (1992) 539-551.
10
[7] T. L. Huntsberger and P. Ajjimarangsee, Parallel self-oraganizing feature maps for unsupervised
11
pattern recognition, Int. J. General System, 16 (1990) 357-372.
12
[8] Y. S. Kim and S. Mitra, An adaptive integrated fuzzy clustering model for pattern recognition, Fuzzy
13
Sets and Systems, 65 (1994) 297-310.
14
[9] Y. S. Kim, An unsupervised neural network using a fuzzy learning rule, Proceedings of 1999 IEEE
15
International Fuzzy Systems, I (1999) 349-353.
16
[10] T. Kohonen, Self-organization and associative memory, 3rd ed., Springer-Verlag, Berlin, (1984)
17
[11] T. Kohonen, The Self-organizing map, Proceedings of the IEEE, 78 (1990) 1464-1480.
18
[12] C-T Lin and C. S. G Lee, Neural fuzzy systems-a neuro-fuzzy synergism to intelligent systems,
19
Prentice-Hall, New Jergy, (1996).
20
[13] B. Moore, ART-1 and pattern clustering, Proceedings of the 1988 Connectionist Models Summer
21
School, (1981) 174-185.
22
[14] S. K. Pal and S. Mitra, Fuzzy dynamic clustering algorithm, Pattern Recognition Letters, 11 (1990)
23
[15] T. J. Ross, Fuzzy logic with engineering applications, McGraw-Hill, New York, (1997).
24
[16] P. K. Simpson, Fuzzy min-max neural network-part 2 : clustering, IEEE Trans. on Fuzzy Systems,
25
[17] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965) 338-352.
26
ORIGINAL_ARTICLE
POINTWISE PSEUDO-METRIC ON THE L-REAL LINE
In this paper, a pointwise pseudo-metric function on the L-realline is constructed. It is proved that the topology induced by this pointwisepseudo-metric is the usual topology.
http://ijfs.usb.ac.ir/article_478_5d05c3a1bafafbe167cbaf76af5b1eec.pdf
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10.22111/ijfs.2005.478
L-topology
Pointwise pseudo-metric
The L-real line
Fu-Gui
Shi
fuguishi@bit.edu.cn or f.g.shi@263.net
true
1
Department of Mathematics, Beijing Institute of Technology, Beijing,
100081, P.R. China
Department of Mathematics, Beijing Institute of Technology, Beijing,
100081, P.R. China
Department of Mathematics, Beijing Institute of Technology, Beijing,
100081, P.R. China
AUTHOR
[1] G. Gierz et al.. , A compendium of continuous lattice, Springer-Verlag, Berlin, 1980.
1
[2] B. Hutton, Normality in fuzzy topological spaces, J. Math. Anal. Appl. , 50 (1975) 74–79.
2
[3] U. H¨ohle, Probabilistsche Metriken auf der Menge nicht negativen verteilungsfunktionen,
3
Aequationes Math. , 18(1978) 345–356.
4
[4] T. E. Gantner, Steinlage R C and Warren R H, Compactness in fuzzy topological spaces, J.
5
Math. Anal. Appl. , 62(1978) 547-562.
6
[5] Y. -M. Liu and M. -K. Luo, Fuzzy topology, World Scientific, Singapore, 1997.
7
[6] F. -G. Shi, Pointwise quasi-uniformities and p.q. metrics on completely distributive lattices,
8
Acta Math. Sinica, 39(1996) 701–706.
9
[7] F. -G. Shi, Pointwise uniformities and metrics on fuzzy lattices, Chinese Sci. Bull. , 42 (1997)
10
718–720.
11
[8] F. -G. Shi, Pointwise uniformities in fuzzy set theory, Fuzzy Sets and Systems, 98(1998)
12
141–146.
13
[9] F. -G. Shi, Pointwise metrics in fuzzy set theory, Fuzzy Sets and Systems, 121(2001) 209–216.
14
ORIGINAL_ARTICLE
DATA ENVELOPMENT ANALYSIS WITH FUZZY RANDOM INPUTS AND OUTPUTS: A CHANCE-CONSTRAINED
PROGRAMMING APPROACH
In this paper, we deal with fuzzy random variables for inputs andoutputs in Data Envelopment Analysis (DEA). These variables are considered as fuzzyrandom flat LR numbers with known distribution. The problem is to find a method forconverting the imprecise chance-constrained DEA model into a crisp one. This can bedone by first, defuzzification of imprecise probability by constructing a suitablemembership function, second, defuzzification of the parameters using an α-cut andfinally, converting the chance-constrained DEA into a crisp model using the methodof Cooper [4].
http://ijfs.usb.ac.ir/article_479_1ced6022a8946914d082e27726e96216.pdf
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10.22111/ijfs.2005.479
Data Envelopment Analysis
Chance-constrained DEA
Fuzzy random
variable
Triangular fuzzy number
SAEED
RAMEZANZADEH
ramezanzadeh_s@yahoo.com
true
1
DEPARTMENT OF MATHEMATICS, POLICE UNIVERSITY, TEHRAN, IRAN
DEPARTMENT OF MATHEMATICS, POLICE UNIVERSITY, TEHRAN, IRAN
DEPARTMENT OF MATHEMATICS, POLICE UNIVERSITY, TEHRAN, IRAN
LEAD_AUTHOR
AZIZOLLAH
MEMARIANI
a_memariani@yahoo.com
true
2
DEPARTMENT OF INDUSTRIAL ENGINEERING, BU-ALI SINA UNIVERSITY,
HAMEDAN, IRAN
DEPARTMENT OF INDUSTRIAL ENGINEERING, BU-ALI SINA UNIVERSITY,
HAMEDAN, IRAN
DEPARTMENT OF INDUSTRIAL ENGINEERING, BU-ALI SINA UNIVERSITY,
HAMEDAN, IRAN
AUTHOR
SABER
SAATI
ssaatim@yahoo.com
true
3
DEPARTMENT OF MATHEMATICS, TEHRAN NORTH BRANCH, ISLAMIC AZAD
UNIVERSITY, TEHRAN, IRAN
DEPARTMENT OF MATHEMATICS, TEHRAN NORTH BRANCH, ISLAMIC AZAD
UNIVERSITY, TEHRAN, IRAN
DEPARTMENT OF MATHEMATICS, TEHRAN NORTH BRANCH, ISLAMIC AZAD
UNIVERSITY, TEHRAN, IRAN
AUTHOR
[1] A. Charnes, W. W. Cooper and G. Yu, Models for dealing with imprecise data in DEA, Managment
1
Science, 45 (1999) 597-607.
2
[2] D. Chakraborty, J. R. Rao and R. N. Tiwari, Multiobjective imprecise chance-constrained
3
programming problem, J. Fuzzy Math, 1 (2) (1993) 377–387. Corrigendum to: Multiobjective
4
imprecise-chance constrained programming problem, J. Fuzzy Math, 2 (1) (1994) 231–232.
5
[3] D. Chakraborty, Redefining chance-constrained programming in fuzzy environment, Fuzzy Sets and
6
Systems , 125 (2002) 327-333.
7
[4] W. W. Cooper, H. Deng, Z. M. Huang and S. X. Li, Satisfying DEA models under Chance constraints,
8
The Annals of Operations Research, 66 (1996a) 279-295.
9
[5] W. W. Cooper, H. Deng, Z. M. Huang and S. X. Li, Chance constrained programming approaches to
10
technical efficiencies and inefficiencies in stochastic data envelopmaent analysis, Journal of the
11
Operational Research Society, 53 (2002a) 1347-1356.
12
[6] W. W. Cooper, H. Deng, Z. M. Huang and S. X. Li, Chance constrained programming approaches to
13
congestion in stochastic data envelopmaent analysis, European Journal of Operational Research,
14
155 (2004) 487-501.
15
[7] W. Guangyvan and Z. Yue, The theory of fuzzy stochastic processes, Fuzzy Sets and Systems, 51
16
(1992) 161-178.
17
[8] W. Guangyuan and Q. Zhong, Linear programming with fuzzy random variable coefficients, FSS, 57
18
(1993) 295-311.
19
[9] P. Gao and H. Tanaka, Fuzzy DEA : A pereceptual evaluation method, Fuzzy Sets and Systems, 119
20
(2001) 149-160.
21
[10] J. L. Hougaard, Fuzzy scores of technical efficiency, European Journal of Operation Research, 115
22
[11] P. Kall and S. W. Wallace, Stochastic Programming, John Wiley &Sons, New York, 1994.
23
[12] C. Kao and S. T. Liu, Fuzzy Efficiency Measures in Data Envelopment Analysis, Fuzzy Sets and
24
Systems, 113 (2000) 529-541.
25
[13] H. Kwakernaak, Fuzzy random variables, definitions and theorems, Inf. Sci., 15 (1978) 1-29.
26
[14] B. Liu, Fuzzy random chance-constrained programming, IEEE Transactions on Fuzzy Systems, 9 (5)
27
(2001) 713–720.
28
[15] B. Liu, Fuzzy random dependent-chance programming, IEEE Transactions on Fuzzy Systems, 9 (5)
29
(2001) 721–726.
30
[16] M.K. Luhandjula, Fuzziness and randomness in an optimization framework, Fuzzy Sets and Systems,
31
77 (1996) 291–297.
32
[17] M. K. Luhandjula and M. M.Gupta, On fuzzy stochastic optimization, Fuzzy Sets and Systems, 81
33
(1996) 47–55.
34
[18] O.B. Olesen and N. C. Petersen, Chance constrained efficiency evaluation, Management Science,
35
41 (1995) 442-457.
36
[19] M. L. Puri and D.A. Ralescu, Fuzzy random variables, J.Math. Anal. Appl., 114 (1986) 409-422.
37
[20] S. Saati, A. Memariani and G. R. Jahanshahloo, Efficiency analysis and ranking of DMUs with fuzzy
38
data, Fuzzy Optimization and Decision Making, 1 (2002) 255-256.
39
[21] B. Seaver and K. Triantis, A fuzzy clustering approach used in evaluating technical efficiency measures
40
in manufacturing, Journal of productivity Analysis, 3 (1992) 337-363.
41
[22] J. K. Sengupta, A Fuzzy System Approach in Data Envelopment Analysis, Computers Math. Applic. 24
42
ORIGINAL_ARTICLE
A SHORT NOTE ON THE RELATIONSHIP BETWEEN GOAL PROGRAMMING AND FUZZY PROGRAMMING FOR
VECTORMAXIMUM PROBLEMS
A theorem was recently introduced to establish a relationship betweengoal programming and fuzzy programming for vectormaximum problems.In this short note it is shown that the relationship does not exist underall circumstances. The necessary correction is proposed.
http://ijfs.usb.ac.ir/article_480_7b6cf03b7e38d16e82e65f5ff0221149.pdf
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10.22111/ijfs.2005.480
Fuzzy programming
Goal programming
Fuzzy multi-objective programming
M. A.
Yaghoobi
yaghoobi@mail.uk.ac.ir
true
1
Faculty of Mathematics and Computer Sciences, University of
Kerman, Kerman, Iran
Faculty of Mathematics and Computer Sciences, University of
Kerman, Kerman, Iran
Faculty of Mathematics and Computer Sciences, University of
Kerman, Kerman, Iran
LEAD_AUTHOR
M.
Tamiz
mehrdad.tamiz@port.ac.uk
true
2
Department of Mathematics, University of Portsmouth, Buckingham Building,
Lion Terrace, Portsmouth, PO1 3HE, UK
Department of Mathematics, University of Portsmouth, Buckingham Building,
Lion Terrace, Portsmouth, PO1 3HE, UK
Department of Mathematics, University of Portsmouth, Buckingham Building,
Lion Terrace, Portsmouth, PO1 3HE, UK
AUTHOR
[1] R. Bellman and L. A. Zadeh, Decision making in a fuzzy environment, Management Sciences,
1
17(4) (1970) B141-B164.
2
[2] A. Charnes and W. W. Cooper, Management models and industrial applications of linear
3
programming, John Wiley and Sons, New York, 1961.
4
[3] M. Ehrgott, Multicriteria optimization, Lecture Notes in Economics and Mathematical Systems,
5
Springer-Verlag, 2000.
6
[4] J. P. Ignizio, Goal programming and extentions, Lexington Books, London, 1976.
7
[5] D. F. Jones and M. Tamiz, Goal programming in the period 1990-2000, In: M. Ehrgott, X.
8
Gandibleux, (Eds.), Multicriteria Optimization: State of the Art Annotated Bibliographic
9
Survey, Kluwer Academic Publisher, Boston, 2002, Chapter 3.
10
[6] H. W. Kuhn and A. W. Tucker, Nonlinear programming, In: J. Neyman, (Ed.), Proceedings
11
of 2nd Berkeley Symposium on Mathematical Statistics and Probabilities, 1951.
12
[7] Y. -J. Lai and C. L. Hwang, Fuzzy Multiple Objective Decision Making: Methods and Applications,
13
Lecture Notes in Economics and Mathematical Systems, Vol. 404, Springer, New
14
York, 1994.
15
[8] R. H. Mohamed, The relationship between goal programming and fuzzy programming, Fuzzy
16
Sets and Systems, 89 (1997) 215-222.
17
[9] B. B. Pal, B. N. Morita and U. Maulik, A goal programming procedure for fuzzy multiobjective
18
linear programming problem, Fuzzy Sets and Systems, 139 (2003) 395-405.
19
[10] R. E. Steuer, Multiple Criteria Optimization: Theory, Computation and Application, John
20
Wiley, New York, 1986.
21
[11] M. Tamiz, D. F. Jones and E. El-Darzi, A review of goal programming and its applications,
22
Annals of Operations Research, 58 (1993) 39-53.
23
[12] M. Tamiz, D. Jones and C. Romero, Goal programming for decision making: An overview of
24
the current state-of-the-art, European Journal of Operational Research, 111 (1998) 569-581.
25
[13] H. J. Zimmerman, Fuzzy programming and linear programming with several objective functions,
26
Fuzzy Sets and Systems, 1 (1978) 45-55.
27
ORIGINAL_ARTICLE
A METHOD FOR SOLVING FUZZY LINEAR SYSTEMS
In this paper we present a method for solving fuzzy linear systemsby two crisp linear systems. Also necessary and sufficient conditions for existenceof solution are given. Some numerical examples illustrate the efficiencyof the method.
http://ijfs.usb.ac.ir/article_481_7287fa5649070a2665e500ffa0d779f1.pdf
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10.22111/ijfs.2005.481
Symmetric fuzzy linear system
Fuzzy linear system
Nonnegative
matrix
Saeid
Abbasbandy
saeid@abbasbandy.com
true
1
Department of Mathematics, Imam Khomeini International University,
Ghazvin, 34194, Iran
Department of Mathematics, Imam Khomeini International University,
Ghazvin, 34194, Iran
Department of Mathematics, Imam Khomeini International University,
Ghazvin, 34194, Iran
LEAD_AUTHOR
Magid
Alavi
alavi_ma2004@yahoo.com
true
2
Department Of Mathematics, Science and Research Branch, Islamic
Azad University, Tehran, 14778, Iran
Department Of Mathematics, Science and Research Branch, Islamic
Azad University, Tehran, 14778, Iran
Department Of Mathematics, Science and Research Branch, Islamic
Azad University, Tehran, 14778, Iran
AUTHOR
[1] T. Allahviranloo, Numerical methods for fuzzy system of linear equations, Appl. Math. Comput.,
1
155 (2004) 493-502.
2
[2] T. Allahviranloo, Successive over relaxation iterative method for fuzzy system of linear equations,
3
Appl. Math. Comput., 162 (2005) 189-196.
4
[3] T. Allahviranloo, The Adomian decomposition method for fuzzy system of linear equations,
5
Appl. Math. Comput., 163 (2005) 553-563.
6
[4] R. Goetschell and W. Voxman, Elementary calculs, Fuzzy Sets and Systems, 18 (1986) 31-43.
7
[5] M. Ma, M. Friedman and A. Kandel, A new fuzzy arithmetic, Fuzzy Sets and Systems, 108
8
(1999) 83-90.
9
[6] H. Minc, Nonnegative Matrices, Wiley, New York, 1988.
10
[7] M. Friedman, Ma Ming and A. Kandel, Fuzzy linear systems, Fuzzy Set and Systems,
11
96(1998) 201-209.
12
[8] G. J. Klir, U. S. Clair and B. Yuan, Fuzzy Set Theory: Foundations and Applications,
13
Prentice-Hall Inc., 1997.
14
ORIGINAL_ARTICLE
A NEURO-FUZZY TECHNIQUE FOR DISCRIMINATION BETWEEN INTERNAL FAULTS AND MAGNETIZING INRUSH CURRENTS IN TRANSFORMERS
This paper presents the application of the fuzzy-neuro method toinvestigate transformer inrush current. Recently, the frequency environment ofpower systems has been made more complicated and the magnitude of the secondharmonic in inrush current has been decreased because of the improvement of caststeel. Therefore, traditional approaches will likely mal-operate in the case ofmagnetizing inrush with low second component and internal faults with highsecond harmonic. The proposed scheme enhances the inrush detection sensitivity ofconventional techniques by using a fuzzy-neuro approach. Details of the designprocedure and the results of performance studies with the proposed detector aregiven in the paper. The results of performance studies show that the proposedalgorithm is fast and accurate.
http://ijfs.usb.ac.ir/article_482_1e80e370d9421c96322a24101a56e88e.pdf
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10.22111/ijfs.2005.482
This paper presents the application of the fuzzy-neuro method to
investigate transformer inrush current. Recently
the frequency environment of
power systems has been made more complicated and the magnitude of the second
harmonic in inrush current has been decreased because of the improvement of cast
steel. Therefore
HASSAN
KHORASHADI-ZADEH
hkhorashadi@birjand.ac.ir
true
1
DEPARTMENT OF POWER ENGINEERING, UNIVERSITY OF BIRJAND,
IRAN
DEPARTMENT OF POWER ENGINEERING, UNIVERSITY OF BIRJAND,
IRAN
DEPARTMENT OF POWER ENGINEERING, UNIVERSITY OF BIRJAND,
IRAN
LEAD_AUTHOR
MOHAMMAD REZA
AGHAEBRAHIMI
aghaebrahimi@birjand.ac.ir
true
2
DEPARTMENT OF POWER ENGINEERING, UNIVERSITY OF
BIRJAND, IRAN
DEPARTMENT OF POWER ENGINEERING, UNIVERSITY OF
BIRJAND, IRAN
DEPARTMENT OF POWER ENGINEERING, UNIVERSITY OF
BIRJAND, IRAN
AUTHOR
[1] U. D. Annakkage and P. G. McLaren et al, A current transformer model based on the Jiles-Atherton
1
theory of ferromagnetic hysteresis, IEEE Trans. Power Delivery, Jan. 2000.
2
[2] D. Chen, W. Chen, X. Yin, Z. Zhang and Y. Hu, The analysis of operation characteristic of
3
transformer differential protection based on virtual third harmonic theory, Proceedings of
4
International Conference on Power System Technology, PowerCon 2002, Vol. 2 , 13-17 Oct. (2002)
5
720 – 722.
6
[3] Electricity Training Association, Power System Protection, Vol. 2, Application, IEE, London, 1995.
7
[4] M. Gomez-Morante and D. W. Nicoletti, A wavelet-based differential transformer protection, IEEE
8
Trans. Power Delivery, Vol. 14, Oct. (1999) 1351–1358.
9
[5] H. Ichihashi, Learning in Hierarchical Fuzzy models by conjugate gradient Methode using
10
Bakpropagation Errors, Proc. of Intelligent System Symp., (1991) 235-240.
11
[6] B. Kasztenny and E. Rosolowski, A self-organizing fuzzy logic based protective relay an application to
12
power transformer protection, IEEE Trans. Power Delivery, Vol. 12, July (1997) 1119–1127.
13
[7] B. Kasztenny, E. Rosolowski and M. Lukowicz, Multi – objective optimization of a neural network
14
based differential relay for power transformers, IEEE transmission and distribution conference, Vo.l2,
15
Apr. (1999) 476-481.
16
[8] M. Kezonuic, A Survey of Neural Net Application to Protective Relaying and Fault Analysis, Eng. Int.
17
Sys. Vol. 5, No. 4, Dec. (1997) 185-192.
18
[9] M. Kezonovic and Y. Guo, Modeling and Simulation of the Power Transformer Faults and Related
19
Protective Relay Behavior, IEEE Trans. Power Delivery, Vol. 15, Jan. (2000) 44–50.
20
[10] H. Khorashadi Zadeh, A Novel Approach to Detection High Impedance Faults Using Artificial Neural
21
Network, Proc. of the 39nd International Universities Power Engineering Conference, UPEC2004, Sep.
22
(2004) 373-377.
23
[11] H. Khorashadi-Zadeh, Correction of Capacitive Voltage Transformer Distorted Secondary Voltages
24
Using Artificial Neural Networks, In Proceedings of Seventh Seminar on Neural Network Applications
25
in Electrical Engineering, Sep. 2004, Belgrad-serbia and Montenegro (Neural 2004).
26
[12] H. Khorashadi-Zadeh and M. R. Aghaebrahimi, AN ANN Based Approach to Improve the Distance
27
Relaying Algorithm, in Proceedings of Cybernetics and Iintelligent Systems Conference, Singapoure,
28
Dec. 2004, (CIS2004).
29
[13] H. Khorashadi Zadeh, Power Transformer Differential Protection Scheme Based on Wavelet
30
Transform and Artificial Neural Network Algorithms, Proc. of the 39nd International Universities
31
Power Engineering Conference, UPEC2004, (2004) 747-753.
32
[14] C. C. Lee, Fuzzy Logic in Control System: Fuzzy Logic controller-part I, IEEE Transmission on
33
System, Man. and Cybernetics, Vol. 20, No.2, 1 April (1990) 404-418.
34
[15] P. Liu, et. al., Study of Non Operation for Internal Faults Of Second-Harmonic Restraint Differential
35
Protection of Power Transformers, Transactions of the Engineering and Operation Division of the
36
Canadian Electrical Association, Vol. 28, Part 4, March (1998) 1-11.
37
[16] P. L. Mao, et al., A novel approach to the classification of the transient phenomena in power
38
transformers using combined wavelet transform and neural network, IEEE Transactions on Power
39
Delivery, Vol. 16, Issue: 2, April (2001) 654 – 659.
40
[17] M. Nagpal, M. S. Sachdev, K. Ning and L.M. Wedephol, Using a neural network for transformer
41
protection, IEEE Proc. of EMPD International Conference, Vol. 2, Nov. (1995) 674-679.
42
[18] L. D. Periz, A. J. Flechsig, J. L. Meador and Z. Obradovic, Training an artificial neural network to
43
discriminate between magnetizing inrush and internal faults, IEEE Trans. Power Delivery, Vol. 9, Jan.
44
(1994) 434–441.
45
[19] PSCAD/EMTDC User’s Manual, Manitoba HVDC Research Center, Winnipeg, Manitoba, Canada.
46
[20] M. A. Rahman and B. Jeyasurya, A state-of-art review of transformer protection algorithm, IEEE
47
Trans. Power Delivery, Vol. 3, Apr. (1988) 534–544.
48
[21] Myong-Chul Shin, Chul-Won Park and Jong-Hyung Kim, Fuzzy logic-based relaying for large power
49
transformer protection, IEEE Transactions on Power Delivery, Vol. 18, Issue: 3, July (2003)
50
718 – 724.
51
[22] Hu Yufeng, Chen Deshu, Yin Xianggen and Zhang Zhe, A novel theory for identifying transformer
52
magnetizing inrush current, Proceedings of International Conference on Power System Technology,
53
PowerCon 2002, Vol. 3 , 13-17 Oct. (2002) 1411-1415.
54
ORIGINAL_ARTICLE
MEASURING SOFTWARE PROCESSES PERFORMANCE BASED ON FUZZY MULTI AGENT MEASUREMENTS
The present article discusses and presents a new and comprehensive approachaimed at measuring the maturity and quality of software processes. This method has beendesigned on the basis of the Software Capability Maturity Model (SW-CMM) and theMulti-level Fuzzy Inference Model and is used as a measurement and analysis tool. Among themost important characteristics of this method one can mention simple usage, accuracy,quantitative measures and comparability. Fuzzy logic-based tools are designed to providesuch functions.
http://ijfs.usb.ac.ir/article_483_1a16c25bab856f9572c194173aa0d2ad.pdf
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70
10.22111/ijfs.2005.483
Software capability maturity model
Goal/ Question / Metric method
Key
process areas
Fuzzy system
Multi level fuzzy inference model
MIR ALI
SEYYEDI
seyyedi@behpardaz.net
true
1
COMPUTER - SOFTWARE DEPARTMENT OF SCIENCES & RESEARCH, TEHRAN,
IRAN
COMPUTER - SOFTWARE DEPARTMENT OF SCIENCES & RESEARCH, TEHRAN,
IRAN
COMPUTER - SOFTWARE DEPARTMENT OF SCIENCES & RESEARCH, TEHRAN,
IRAN
LEAD_AUTHOR
MOHAMMA
TESHNEHLAB
teshnehlab@eet.kntu.ac.ir
true
2
DEPARTMENT OF CONTROL, KHAJEH NASIR TECHNICAL UNIVERSITY,
TEHRAN, IRAN
DEPARTMENT OF CONTROL, KHAJEH NASIR TECHNICAL UNIVERSITY,
TEHRAN, IRAN
DEPARTMENT OF CONTROL, KHAJEH NASIR TECHNICAL UNIVERSITY,
TEHRAN, IRAN
AUTHOR
FEREIDOON
SHAMS
f.shams@agri-jahad.org
true
3
COMPUTER - SOFTWARE DEPARTMENT OF SCIENCES & RESEARCH, TEHRAN,
IRAN
COMPUTER - SOFTWARE DEPARTMENT OF SCIENCES & RESEARCH, TEHRAN,
IRAN
COMPUTER - SOFTWARE DEPARTMENT OF SCIENCES & RESEARCH, TEHRAN,
IRAN
AUTHOR
[1] Alexander, Distributed fuzzy Control of ultivariable systems, Klawer academic Publishers, 1996.
1
[2] V. R. Basili, C. Caldiera, and D. Rombach, Experience Factory, Encyclopedia of Software Engineering
2
Volume 1, ,Jogn Wiley & Sons, (1994) 469-476.
3
[3] V. R. Basili and H. D. Rombach, The TAME Project: Towards improvement – oriented software
4
environments, in IEEE Transactions on Software Engineering, 14 (6), (1988) 758-773.
5
[4] J. H. Baumert and M. S. McWhinney, Software easures and the capability Maturity Model. Software
6
Engineering Institute Technical Report, CMU/SEI-92-TR-25, ESC-TR-92-0, 1992.
7
[5] C. Bergstrom, 2000, process Metrics for Ericsson Erisoft AB-a proposal, Umea University Report
8
Umnad 292/2000, Umea, Jan.
9
[6] Gregory E. Kersten, Stan Szpakowiz, Negotration in distributed artificial intelligence, IEEE 1994 .
10
[7] P. Kuvaja and A. Bicego, BOOTSTRAP-Europe’s Assessment Method, IEEE Software, May (1993) 83-
11
[8] M. C. Paulk, C. V. Weber, S. Garcia, M. B. Chrissis and M. Bush, key Practices of the Capability
12
Maturity Model Version 1.1, Software Engineering Institute Technical Report, CMU/SEI-93-TR-25
13
ESC-TR-93-178, Pittsburgh, PA, 1993.
14
[9] J. Raynus, Software Process Improvement With CMM, Artech House Publishers, 1999, Boston.
15
[10] R. Van Solingen and E. Berghout, the Goal/Question/Metric Method- A Practical Guide for Quality
16
Improvement of Software development”, McGRAW-Hill Companiew, London 1999 .
17
ORIGINAL_ARTICLE
ON ANTI FUZZY IDEALS IN NEAR-RINGS
In this paper, we apply the Biswas’ idea of anti fuzzy subgroups toideals of near-rings. We introduce the notion of anti fuzzy ideals of near-rings,and investigate some related properties.
http://ijfs.usb.ac.ir/article_484_4fb52b20fde87511ab0820dd4e44ad02.pdf
2005-10-21T11:23:20
2018-05-23T11:23:20
71
80
10.22111/ijfs.2005.484
near-ring
anti fuzzy subnear-ring
anti (fuzzy) right (resp. left) ideals
anti level right (resp. left) ideals
Kyung Ho
Kim
ghkim@chungju.ac.kr
true
1
Department of Mathematics, Chungju National University, Chungju
380-702, Korea
Department of Mathematics, Chungju National University, Chungju
380-702, Korea
Department of Mathematics, Chungju National University, Chungju
380-702, Korea
AUTHOR
Young Bae
Jun
ybjun@nongae.gsnu.ac.kr
true
2
Department of Mathematics Education, Gyeongsang National University,
Chinju 660-701, Korea
Department of Mathematics Education, Gyeongsang National University,
Chinju 660-701, Korea
Department of Mathematics Education, Gyeongsang National University,
Chinju 660-701, Korea
AUTHOR
Yong Ho
Yon
yhyonkr@hanmail.net
true
3
Department of Mathematics, Chungbuk National University, Cheongju
361-763, Korea
Department of Mathematics, Chungbuk National University, Cheongju
361-763, Korea
Department of Mathematics, Chungbuk National University, Cheongju
361-763, Korea
AUTHOR
[1] S. Abou-Zaid, On fuzzy subnear-rings and ideals, Fuzzy Sets and Sys 44, (1991), 139-146.
1
[2] R. Biswas, Fuzzy subgroups and anti fuzzy subgroups, Fuzzy Sets and Sys 35, (1990), 121-124.
2
[3] P. S. Das, Fuzzy groups and level subgroups, J. Math. Anal. and Appl. 84 , (1981), 264-269.
3
[4] V. N. Dixit, R. Kumar and N. Ajmal, Fuzzy ideals and fuzzy prime ideals of a ring, Fuzzy
4
Sets and Sys 44 , (1991), 127-138.
5
[5] V. N. Dixit, R. Kumar and N. Ajmal, On fuzzy rings, Fuzzy Sets and Systems 49 , (1992),
6
[6] S. M. Hong, Y. B. Jun and H. S. Kim, Fuzzy ideals in near-rings, Bull. Korean Math. Soc.
7
35 (No. 3), (1998), 455-464.
8
[7] C. K. Hur and H. S. Kim, On fuzzy relations of near-rings, Far East J. Math. Sci. (to appear).
9
[8] Y. B. Jun and H. S. Kim, On fuzzy prime ideals under near-ring homomorphisms, (submitted).
10
[9] K. H. Kim and Y. B. Jun, Anti fuzzy R-subgroups of near-rings, Scientiae Mathematicae 2
11
(No.2 ) , (1999), 147-153.
12
[10] S. D. Kim and H. S. Kim , On fuzzy ideals of near-rings, Bull. Korean Math. Soc. 33, (1996),
13
[11] C. K. Kim and H. S. Kim, On normalized fuzzy ideals of near-rings, Far East J. Math. Sci.
14
[12] R. Kumar , Fuzzy irreducible ideals in rings, Fuzzy Sets and Systems 42, (1991), 369-379.
15
[13] R. Kumar, Certain fuzzy ideals of rings redefined, Fuzzy Sets and Systems 46, (1992), 251-
16
[14] W. Liu, Fuzzy invariant subgroups and fuzzy ideals, Fuzzy Sets and Systems 8, (1982), 133-
17
[15] D. S. Mailk, Fuzzy ideals of artinian rings, Fuzzy Sets and Systems 37, (1990), 111-115.
18
[16] J. D. P. Meldrum, Near-rings and their links with groups, Pitman, Boston , (1985).
19
[17] A. Rosenfeld, Fuzzy groups, J. Math. Anal. Appl. 35, (1971), 512-517.
20
[18] L. A. Zadeh, Fuzzy sets, Inform. and Control. 8, (1965), 338-353.
21
ORIGINAL_ARTICLE
Persian-translation vol.2, no.2 October 2005
http://ijfs.usb.ac.ir/article_3124_0cb94c10119b1b62f819725b5ccefa3b.pdf
2005-10-29T11:23:20
2018-05-23T11:23:20
83
90
10.22111/ijfs.2005.3124