2005
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INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES
INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES
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2
The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART1(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.
1
The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART1(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.
1
13
YONG SOO
KIM
YONG SOO
KIM
DIVISION OF COMPUTER ENGINEERING, DAEJEON UNIVERSITY, DAEJEON, 300716,
KOREA
DIVISION OF COMPUTER ENGINEERING, DAEJEON
Korea
kystj@dju.ac.kr
Z.
ZENN BIEN
Z.
ZENN BIEN
DEPARTMENT OF ELECRICAL ENGINEERING AND COMPUTER SCIENCE, KAIST,
DAEJEON, 305701, KOREA
DEPARTMENT OF ELECRICAL ENGINEERING AND COMPUTER
Korea
zbien@ee.kaist.ac.kr
Neural Networks
fuzzy logic
Fuzzy neural networks
Learning rule
Fuzzification
[[1] J. C. Bezdek, E. C. Tsao and N. R. Pal, Fuzzy Kohonen clustering networks, Proceeding of the First##IEEE Conference on Fuzzy System, (1992) 10351043.##[2] J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York,##[3] G. A. Carpenter and S. Grossberg, A massively parallel architecture for a selforganization neural##pattern recognition machine, Computer vision, Graphics, and Image processing, 37 (1987) 54115.##[4] G. A. Capenter, S. Grossberg and D. B. Rosen, Fuzzy ART : fast stable learning and categorization##of analog pattern by an adaptive resonance systems, Neural Networks, 4 (1992) 759772.##[5] FL Chung and T. Lee, A fuzzy learning model for membership function estimation and pattern##classification, Proceedings of the third IEEE conference on Fuzzy systems, 1 (1994) 426431.##[6] F. L. Chung and T. Lee, Fuzzy competitive learning, Neural Networks, 7 (1992) 539551.##[7] T. L. Huntsberger and P. Ajjimarangsee, Parallel selforaganizing feature maps for unsupervised##pattern recognition, Int. J. General System, 16 (1990) 357372.##[8] Y. S. Kim and S. Mitra, An adaptive integrated fuzzy clustering model for pattern recognition, Fuzzy ##Sets and Systems, 65 (1994) 297310.##[9] Y. S. Kim, An unsupervised neural network using a fuzzy learning rule, Proceedings of 1999 IEEE##International Fuzzy Systems, I (1999) 349353.##[10] T. Kohonen, Selforganization and associative memory, 3rd ed., SpringerVerlag, Berlin, (1984)##[11] T. Kohonen, The Selforganizing map, Proceedings of the IEEE, 78 (1990) 14641480.##[12] CT Lin and C. S. G Lee, Neural fuzzy systemsa neurofuzzy synergism to intelligent systems,##PrenticeHall, New Jergy, (1996).##[13] B. Moore, ART1 and pattern clustering, Proceedings of the 1988 Connectionist Models Summer##School, (1981) 174185.##[14] S. K. Pal and S. Mitra, Fuzzy dynamic clustering algorithm, Pattern Recognition Letters, 11 (1990)##[15] T. J. Ross, Fuzzy logic with engineering applications, McGrawHill, New York, (1997).##[16] P. K. Simpson, Fuzzy minmax neural networkpart 2 : clustering, IEEE Trans. on Fuzzy Systems,##[17] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965) 338352.##]
POINTWISE PSEUDOMETRIC ON THE LREAL LINE
POINTWISE PSEUDOMETRIC ON THE LREAL LINE
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2
In this paper, a pointwise pseudometric function on the Lrealline is constructed. It is proved that the topology induced by this pointwisepseudometric is the usual topology.
1
In this paper, a pointwise pseudometric function on the Lrealline is constructed. It is proved that the topology induced by this pointwisepseudometric is the usual topology.
15
20
FuGui
Shi
FuGui
Shi
Department of Mathematics, Beijing Institute of Technology, Beijing,
100081, P.R. China
Department of Mathematics, Beijing Institute
China
fuguishi@bit.edu.cn or f.g.shi@263.net
Ltopology
Pointwise pseudometric
The Lreal line
[[1] G. Gierz et al.. , A compendium of continuous lattice, SpringerVerlag, Berlin, 1980.##[2] B. Hutton, Normality in fuzzy topological spaces, J. Math. Anal. Appl. , 50 (1975) 74–79.##[3] U. H¨ohle, Probabilistsche Metriken auf der Menge nicht negativen verteilungsfunktionen,##Aequationes Math. , 18(1978) 345–356.##[4] T. E. Gantner, Steinlage R C and Warren R H, Compactness in fuzzy topological spaces, J.##Math. Anal. Appl. , 62(1978) 547562.##[5] Y. M. Liu and M. K. Luo, Fuzzy topology, World Scientific, Singapore, 1997.##[6] F. G. Shi, Pointwise quasiuniformities and p.q. metrics on completely distributive lattices,##Acta Math. Sinica, 39(1996) 701–706.##[7] F. G. Shi, Pointwise uniformities and metrics on fuzzy lattices, Chinese Sci. Bull. , 42 (1997)##718–720.##[8] F. G. Shi, Pointwise uniformities in fuzzy set theory, Fuzzy Sets and Systems, 98(1998)##141–146.##[9] F. G. Shi, Pointwise metrics in fuzzy set theory, Fuzzy Sets and Systems, 121(2001) 209–216.##]
DATA ENVELOPMENT ANALYSIS WITH FUZZY RANDOM INPUTS AND OUTPUTS: A CHANCECONSTRAINED
PROGRAMMING APPROACH
DATA ENVELOPMENT ANALYSIS WITH FUZZY RANDOM INPUTS AND OUTPUTS: A CHANCECONSTRAINED
PROGRAMMING APPROACH
2
2
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 chanceconstrained 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 αcut andfinally, converting the chanceconstrained DEA into a crisp model using the methodof Cooper [4].
1
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 chanceconstrained 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 αcut andfinally, converting the chanceconstrained DEA into a crisp model using the methodof Cooper [4].
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SAEED
RAMEZANZADEH
SAEED
RAMEZANZADEH
DEPARTMENT OF MATHEMATICS, POLICE UNIVERSITY, TEHRAN, IRAN
DEPARTMENT OF MATHEMATICS, POLICE UNIVERSITY,
Iran
ramezanzadeh_s@yahoo.com
AZIZOLLAH
MEMARIANI
AZIZOLLAH
MEMARIANI
DEPARTMENT OF INDUSTRIAL ENGINEERING, BUALI SINA UNIVERSITY,
HAMEDAN, IRAN
DEPARTMENT OF INDUSTRIAL ENGINEERING, BUALI
Iran
a_memariani@yahoo.com
SABER
SAATI
SABER
SAATI
DEPARTMENT OF MATHEMATICS, TEHRAN NORTH BRANCH, ISLAMIC AZAD
UNIVERSITY, TEHRAN, IRAN
DEPARTMENT OF MATHEMATICS, TEHRAN NORTH BRANCH,
Iran
ssaatim@yahoo.com
Data Envelopment Analysis
Chanceconstrained DEA
Fuzzy random variable
Triangular fuzzy number
[[1] A. Charnes, W. W. Cooper and G. Yu, Models for dealing with imprecise data in DEA, Managment##Science, 45 (1999) 597607.##[2] D. Chakraborty, J. R. Rao and R. N. Tiwari, Multiobjective imprecise chanceconstrained##programming problem, J. Fuzzy Math, 1 (2) (1993) 377–387. Corrigendum to: Multiobjective##imprecisechance constrained programming problem, J. Fuzzy Math, 2 (1) (1994) 231–232.##[3] D. Chakraborty, Redefining chanceconstrained programming in fuzzy environment, Fuzzy Sets and##Systems , 125 (2002) 327333.##[4] W. W. Cooper, H. Deng, Z. M. Huang and S. X. Li, Satisfying DEA models under Chance constraints,##The Annals of Operations Research, 66 (1996a) 279295. ##[5] W. W. Cooper, H. Deng, Z. M. Huang and S. X. Li, Chance constrained programming approaches to##technical efficiencies and inefficiencies in stochastic data envelopmaent analysis, Journal of the##Operational Research Society, 53 (2002a) 13471356.##[6] W. W. Cooper, H. Deng, Z. M. Huang and S. X. Li, Chance constrained programming approaches to##congestion in stochastic data envelopmaent analysis, European Journal of Operational Research,##155 (2004) 487501.##[7] W. Guangyvan and Z. Yue, The theory of fuzzy stochastic processes, Fuzzy Sets and Systems, 51##(1992) 161178.##[8] W. Guangyuan and Q. Zhong, Linear programming with fuzzy random variable coefficients, FSS, 57##(1993) 295311.##[9] P. Gao and H. Tanaka, Fuzzy DEA : A pereceptual evaluation method, Fuzzy Sets and Systems, 119##(2001) 149160.##[10] J. L. Hougaard, Fuzzy scores of technical efficiency, European Journal of Operation Research, 115##[11] P. Kall and S. W. Wallace, Stochastic Programming, John Wiley &Sons, New York, 1994.##[12] C. Kao and S. T. Liu, Fuzzy Efficiency Measures in Data Envelopment Analysis, Fuzzy Sets and##Systems, 113 (2000) 529541.##[13] H. Kwakernaak, Fuzzy random variables, definitions and theorems, Inf. Sci., 15 (1978) 129.##[14] B. Liu, Fuzzy random chanceconstrained programming, IEEE Transactions on Fuzzy Systems, 9 (5)##(2001) 713–720.##[15] B. Liu, Fuzzy random dependentchance programming, IEEE Transactions on Fuzzy Systems, 9 (5)##(2001) 721–726.##[16] M.K. Luhandjula, Fuzziness and randomness in an optimization framework, Fuzzy Sets and Systems,##77 (1996) 291–297.##[17] M. K. Luhandjula and M. M.Gupta, On fuzzy stochastic optimization, Fuzzy Sets and Systems, 81##(1996) 47–55.##[18] O.B. Olesen and N. C. Petersen, Chance constrained efficiency evaluation, Management Science,##41 (1995) 442457.##[19] M. L. Puri and D.A. Ralescu, Fuzzy random variables, J.Math. Anal. Appl., 114 (1986) 409422.##[20] S. Saati, A. Memariani and G. R. Jahanshahloo, Efficiency analysis and ranking of DMUs with fuzzy##data, Fuzzy Optimization and Decision Making, 1 (2002) 255256.##[21] B. Seaver and K. Triantis, A fuzzy clustering approach used in evaluating technical efficiency measures##in manufacturing, Journal of productivity Analysis, 3 (1992) 337363.##[22] J. K. Sengupta, A Fuzzy System Approach in Data Envelopment Analysis, Computers Math. Applic. 24##]
A SHORT NOTE ON THE RELATIONSHIP BETWEEN GOAL PROGRAMMING AND FUZZY PROGRAMMING FOR
VECTORMAXIMUM PROBLEMS
A SHORT NOTE ON THE RELATIONSHIP BETWEEN GOAL PROGRAMMING AND FUZZY PROGRAMMING FOR
VECTORMAXIMUM PROBLEMS
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2
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.
1
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.
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36
M. A.
Yaghoobi
M. A.
Yaghoobi
Faculty of Mathematics and Computer Sciences, University of
Kerman, Kerman, Iran
Faculty of Mathematics and Computer Sciences,
Iran
yaghoobi@mail.uk.ac.ir
M.
Tamiz
M.
Tamiz
Department of Mathematics, University of Portsmouth, Buckingham Building,
Lion Terrace, Portsmouth, PO1 3HE, UK
Department of Mathematics, University of
United Kingdom
mehrdad.tamiz@port.ac.uk
Fuzzy programming
Goal programming
Fuzzy multiobjective programming
[[1] R. Bellman and L. A. Zadeh, Decision making in a fuzzy environment, Management Sciences,##17(4) (1970) B141B164.##[2] A. Charnes and W. W. Cooper, Management models and industrial applications of linear##programming, John Wiley and Sons, New York, 1961.##[3] M. Ehrgott, Multicriteria optimization, Lecture Notes in Economics and Mathematical Systems,##SpringerVerlag, 2000.##[4] J. P. Ignizio, Goal programming and extentions, Lexington Books, London, 1976.##[5] D. F. Jones and M. Tamiz, Goal programming in the period 19902000, In: M. Ehrgott, X.##Gandibleux, (Eds.), Multicriteria Optimization: State of the Art Annotated Bibliographic##Survey, Kluwer Academic Publisher, Boston, 2002, Chapter 3.##[6] H. W. Kuhn and A. W. Tucker, Nonlinear programming, In: J. Neyman, (Ed.), Proceedings##of 2nd Berkeley Symposium on Mathematical Statistics and Probabilities, 1951.##[7] Y. J. Lai and C. L. Hwang, Fuzzy Multiple Objective Decision Making: Methods and Applications,##Lecture Notes in Economics and Mathematical Systems, Vol. 404, Springer, New##York, 1994.##[8] R. H. Mohamed, The relationship between goal programming and fuzzy programming, Fuzzy##Sets and Systems, 89 (1997) 215222.##[9] B. B. Pal, B. N. Morita and U. Maulik, A goal programming procedure for fuzzy multiobjective##linear programming problem, Fuzzy Sets and Systems, 139 (2003) 395405.##[10] R. E. Steuer, Multiple Criteria Optimization: Theory, Computation and Application, John##Wiley, New York, 1986.##[11] M. Tamiz, D. F. Jones and E. ElDarzi, A review of goal programming and its applications,##Annals of Operations Research, 58 (1993) 3953.##[12] M. Tamiz, D. Jones and C. Romero, Goal programming for decision making: An overview of##the current stateoftheart, European Journal of Operational Research, 111 (1998) 569581.##[13] H. J. Zimmerman, Fuzzy programming and linear programming with several objective functions,##Fuzzy Sets and Systems, 1 (1978) 4555.##]
A METHOD FOR SOLVING FUZZY LINEAR SYSTEMS
A METHOD FOR SOLVING FUZZY LINEAR SYSTEMS
2
2
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.
1
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.
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43
Saeid
Abbasbandy
Saeid
Abbasbandy
Department of Mathematics, Imam Khomeini International University,
Ghazvin, 34194, Iran
Department of Mathematics, Imam Khomeini
Iran
saeid@abbasbandy.com
Magid
Alavi
Magid
Alavi
Department Of Mathematics, Science and Research Branch, Islamic
Azad University, Tehran, 14778, Iran
Department Of Mathematics, Science and Research
Iran
alavi_ma2004@yahoo.com
Symmetric fuzzy linear system
Fuzzy linear system
Nonnegative matrix
[[1] T. Allahviranloo, Numerical methods for fuzzy system of linear equations, Appl. Math. Comput.,##155 (2004) 493502.##[2] T. Allahviranloo, Successive over relaxation iterative method for fuzzy system of linear equations,##Appl. Math. Comput., 162 (2005) 189196.##[3] T. Allahviranloo, The Adomian decomposition method for fuzzy system of linear equations,##Appl. Math. Comput., 163 (2005) 553563.##[4] R. Goetschell and W. Voxman, Elementary calculs, Fuzzy Sets and Systems, 18 (1986) 3143.##[5] M. Ma, M. Friedman and A. Kandel, A new fuzzy arithmetic, Fuzzy Sets and Systems, 108##(1999) 8390.##[6] H. Minc, Nonnegative Matrices, Wiley, New York, 1988.##[7] M. Friedman, Ma Ming and A. Kandel, Fuzzy linear systems, Fuzzy Set and Systems,##96(1998) 201209. ##[8] G. J. Klir, U. S. Clair and B. Yuan, Fuzzy Set Theory: Foundations and Applications,##PrenticeHall Inc., 1997.##]
A NEUROFUZZY TECHNIQUE FOR DISCRIMINATION BETWEEN INTERNAL FAULTS AND MAGNETIZING INRUSH CURRENTS IN TRANSFORMERS
A NEUROFUZZY TECHNIQUE FOR DISCRIMINATION BETWEEN INTERNAL FAULTS AND MAGNETIZING INRUSH CURRENTS IN TRANSFORMERS
2
2
This paper presents the application of the fuzzyneuro 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 maloperate 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 fuzzyneuro 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.
1
This paper presents the application of the fuzzyneuro 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 maloperate 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 fuzzyneuro 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.
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HASSAN
KHORASHADIZADEH
HASSAN
KHORASHADIZADEH
DEPARTMENT OF POWER ENGINEERING, UNIVERSITY OF BIRJAND,
IRAN
DEPARTMENT OF POWER ENGINEERING, UNIVERSITY
Iran
hkhorashadi@birjand.ac.ir
MOHAMMAD REZA
AGHAEBRAHIMI
MOHAMMAD REZA
AGHAEBRAHIMI
DEPARTMENT OF POWER ENGINEERING, UNIVERSITY OF
BIRJAND, IRAN
DEPARTMENT OF POWER ENGINEERING, UNIVERSITY
Iran
aghaebrahimi@birjand.ac.ir
This paper presents the application of the fuzzyneuro 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
[[1] U. D. Annakkage and P. G. McLaren et al, A current transformer model based on the JilesAtherton##theory of ferromagnetic hysteresis, IEEE Trans. Power Delivery, Jan. 2000. ##[2] D. Chen, W. Chen, X. Yin, Z. Zhang and Y. Hu, The analysis of operation characteristic of##transformer differential protection based on virtual third harmonic theory, Proceedings of##International Conference on Power System Technology, PowerCon 2002, Vol. 2 , 1317 Oct. (2002)##720 – 722.##[3] Electricity Training Association, Power System Protection, Vol. 2, Application, IEE, London, 1995.##[4] M. GomezMorante and D. W. Nicoletti, A waveletbased differential transformer protection, IEEE##Trans. Power Delivery, Vol. 14, Oct. (1999) 1351–1358.##[5] H. Ichihashi, Learning in Hierarchical Fuzzy models by conjugate gradient Methode using##Bakpropagation Errors, Proc. of Intelligent System Symp., (1991) 235240.##[6] B. Kasztenny and E. Rosolowski, A selforganizing fuzzy logic based protective relay an application to##power transformer protection, IEEE Trans. Power Delivery, Vol. 12, July (1997) 1119–1127.##[7] B. Kasztenny, E. Rosolowski and M. Lukowicz, Multi – objective optimization of a neural network##based differential relay for power transformers, IEEE transmission and distribution conference, Vo.l2,##Apr. (1999) 476481.##[8] M. Kezonuic, A Survey of Neural Net Application to Protective Relaying and Fault Analysis, Eng. Int.##Sys. Vol. 5, No. 4, Dec. (1997) 185192.##[9] M. Kezonovic and Y. Guo, Modeling and Simulation of the Power Transformer Faults and Related##Protective Relay Behavior, IEEE Trans. Power Delivery, Vol. 15, Jan. (2000) 44–50.##[10] H. Khorashadi Zadeh, A Novel Approach to Detection High Impedance Faults Using Artificial Neural##Network, Proc. of the 39nd International Universities Power Engineering Conference, UPEC2004, Sep.##(2004) 373377.##[11] H. KhorashadiZadeh, Correction of Capacitive Voltage Transformer Distorted Secondary Voltages##Using Artificial Neural Networks, In Proceedings of Seventh Seminar on Neural Network Applications##in Electrical Engineering, Sep. 2004, Belgradserbia and Montenegro (Neural 2004).##[12] H. KhorashadiZadeh and M. R. Aghaebrahimi, AN ANN Based Approach to Improve the Distance##Relaying Algorithm, in Proceedings of Cybernetics and Iintelligent Systems Conference, Singapoure,##Dec. 2004, (CIS2004).##[13] H. Khorashadi Zadeh, Power Transformer Differential Protection Scheme Based on Wavelet##Transform and Artificial Neural Network Algorithms, Proc. of the 39nd International Universities##Power Engineering Conference, UPEC2004, (2004) 747753.##[14] C. C. Lee, Fuzzy Logic in Control System: Fuzzy Logic controllerpart I, IEEE Transmission on##System, Man. and Cybernetics, Vol. 20, No.2, 1 April (1990) 404418.##[15] P. Liu, et. al., Study of Non Operation for Internal Faults Of SecondHarmonic Restraint Differential##Protection of Power Transformers, Transactions of the Engineering and Operation Division of the##Canadian Electrical Association, Vol. 28, Part 4, March (1998) 111.##[16] P. L. Mao, et al., A novel approach to the classification of the transient phenomena in power##transformers using combined wavelet transform and neural network, IEEE Transactions on Power##Delivery, Vol. 16, Issue: 2, April (2001) 654 – 659.##[17] M. Nagpal, M. S. Sachdev, K. Ning and L.M. Wedephol, Using a neural network for transformer##protection, IEEE Proc. of EMPD International Conference, Vol. 2, Nov. (1995) 674679.##[18] L. D. Periz, A. J. Flechsig, J. L. Meador and Z. Obradovic, Training an artificial neural network to##discriminate between magnetizing inrush and internal faults, IEEE Trans. Power Delivery, Vol. 9, Jan.##(1994) 434–441.##[19] PSCAD/EMTDC User’s Manual, Manitoba HVDC Research Center, Winnipeg, Manitoba, Canada.##[20] M. A. Rahman and B. Jeyasurya, A stateofart review of transformer protection algorithm, IEEE##Trans. Power Delivery, Vol. 3, Apr. (1988) 534–544.##[21] MyongChul Shin, ChulWon Park and JongHyung Kim, Fuzzy logicbased relaying for large power##transformer protection, IEEE Transactions on Power Delivery, Vol. 18, Issue: 3, July (2003)##718 – 724.##[22] Hu Yufeng, Chen Deshu, Yin Xianggen and Zhang Zhe, A novel theory for identifying transformer##magnetizing inrush current, Proceedings of International Conference on Power System Technology,##PowerCon 2002, Vol. 3 , 1317 Oct. (2002) 14111415.##]
MEASURING SOFTWARE PROCESSES PERFORMANCE BASED ON FUZZY MULTI AGENT MEASUREMENTS
MEASURING SOFTWARE PROCESSES PERFORMANCE BASED ON FUZZY MULTI AGENT MEASUREMENTS
2
2
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 (SWCMM) and theMultilevel 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 logicbased tools are designed to providesuch functions.
1
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 (SWCMM) and theMultilevel 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 logicbased tools are designed to providesuch functions.
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70
MIR ALI
SEYYEDI
MIR ALI
SEYYEDI
COMPUTER  SOFTWARE DEPARTMENT OF SCIENCES & RESEARCH, TEHRAN,
IRAN
COMPUTER  SOFTWARE DEPARTMENT OF SCIENCES
Iran
seyyedi@behpardaz.net
MOHAMMA
TESHNEHLAB
MOHAMMA
TESHNEHLAB
DEPARTMENT OF CONTROL, KHAJEH NASIR TECHNICAL UNIVERSITY,
TEHRAN, IRAN
DEPARTMENT OF CONTROL, KHAJEH NASIR TECHNICAL
Iran
teshnehlab@eet.kntu.ac.ir
FEREIDOON
SHAMS
FEREIDOON
SHAMS
COMPUTER  SOFTWARE DEPARTMENT OF SCIENCES & RESEARCH, TEHRAN,
IRAN
COMPUTER  SOFTWARE DEPARTMENT OF SCIENCES
Iran
f.shams@agrijahad.org
Software capability maturity model
Goal/ Question / Metric method
Key process areas
Fuzzy system
Multi level fuzzy inference model
[[1] Alexander, Distributed fuzzy Control of ultivariable systems, Klawer academic Publishers, 1996.##[2] V. R. Basili, C. Caldiera, and D. Rombach, Experience Factory, Encyclopedia of Software Engineering##Volume 1, ,Jogn Wiley & Sons, (1994) 469476.##[3] V. R. Basili and H. D. Rombach, The TAME Project: Towards improvement – oriented software##environments, in IEEE Transactions on Software Engineering, 14 (6), (1988) 758773.##[4] J. H. Baumert and M. S. McWhinney, Software easures and the capability Maturity Model. Software##Engineering Institute Technical Report, CMU/SEI92TR25, ESCTR920, 1992.##[5] C. Bergstrom, 2000, process Metrics for Ericsson Erisoft ABa proposal, Umea University Report##Umnad 292/2000, Umea, Jan.##[6] Gregory E. Kersten, Stan Szpakowiz, Negotration in distributed artificial intelligence, IEEE 1994 .##[7] P. Kuvaja and A. Bicego, BOOTSTRAPEurope’s Assessment Method, IEEE Software, May (1993) 83##[8] M. C. Paulk, C. V. Weber, S. Garcia, M. B. Chrissis and M. Bush, key Practices of the Capability##Maturity Model Version 1.1, Software Engineering Institute Technical Report, CMU/SEI93TR25##ESCTR93178, Pittsburgh, PA, 1993.##[9] J. Raynus, Software Process Improvement With CMM, Artech House Publishers, 1999, Boston.##[10] R. Van Solingen and E. Berghout, the Goal/Question/Metric Method A Practical Guide for Quality##Improvement of Software development”, McGRAWHill Companiew, London 1999 .##]
ON ANTI FUZZY IDEALS IN NEARRINGS
ON ANTI FUZZY IDEALS IN NEARRINGS
2
2
In this paper, we apply the Biswas’ idea of anti fuzzy subgroups toideals of nearrings. We introduce the notion of anti fuzzy ideals of nearrings,and investigate some related properties.
1
In this paper, we apply the Biswas’ idea of anti fuzzy subgroups toideals of nearrings. We introduce the notion of anti fuzzy ideals of nearrings,and investigate some related properties.
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80
Kyung Ho
Kim
Kyung Ho
Kim
Department of Mathematics, Chungju National University, Chungju
380702, Korea
Department of Mathematics, Chungju National
Korea
ghkim@chungju.ac.kr
Young Bae
Jun
Young Bae
Jun
Department of Mathematics Education, Gyeongsang National University,
Chinju 660701, Korea
Department of Mathematics Education, Gyeongsang
Korea
ybjun@nongae.gsnu.ac.kr
Yong Ho
Yon
Yong Ho
Yon
Department of Mathematics, Chungbuk National University, Cheongju
361763, Korea
Department of Mathematics, Chungbuk National
Korea
yhyonkr@hanmail.net
nearring
anti fuzzy subnearring
anti (fuzzy) right (resp. left) ideals
anti level right (resp. left) ideals
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