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Volumn 37, Issue 9, 2009, Pages 742-752

Flow discharge modeling in open canals using a new fuzzy modeling technique (SMRGT)

Author keywords

Contour map; Flow discharge; Fuzzy logic; Modeling; Open canal; Optimal design

Indexed keywords

ALGORITHM; CANAL; CONTOUR MAP; DESIGN; DISCHARGE; FLOW MODELING; FUZZY MATHEMATICS; OPEN CHANNEL FLOW; WATER FLOW;

EID: 70349604899     PISSN: 18630650     EISSN: None     Source Type: Journal    
DOI: 10.1002/clen.200900146     Document Type: Article
Times cited : (27)

References (84)
  • 1
    • 70349590881 scopus 로고    scopus 로고
    • Errors in Measuring the Flow Discharge in a Trapezoidal Open Canal, Rural
    • Y. Ermolin, Errors in Measuring the Flow Discharge in a Trapezoidal Open Canal, Rural Environ. Eng. 2000, 39, 74-81.
    • (2000) Environ. Eng. , vol.39 , pp. 74-81
    • Ermolin, Y.1
  • 2
    • 70349601525 scopus 로고
    • Application of the Manning and Strickler Formula to Land Improvement Projects in Cote-D'Ivoire
    • B. Dubos, Application of the Manning and Strickler Formula to Land Improvement Projects in Cote-D'Ivoire, Oleagineux 1988, 43 (2), 51-53.
    • (1988) Oleagineux , vol.43 , Issue.2 , pp. 51-53
    • Dubos, B.1
  • 3
    • 33947609146 scopus 로고    scopus 로고
    • Development of Artificial Neural Network Model for Simulating the Flow Behavior in Open Channel Infested by Submerged Aquatic Weeds
    • M. A. M. Abdeen, Development of Artificial Neural Network Model for Simulating the Flow Behavior in Open Channel Infested by Submerged Aquatic Weeds, J. Mech. Sci. Tech. 2006, 20 (10), 1576-1589.
    • (2006) J. Mech. Sci. Tech. , vol.20 , Issue.10 , pp. 1576-1589
    • Abdeen, M.A.M.1
  • 4
    • 33748806571 scopus 로고    scopus 로고
    • Optimal Design of Open Channel Section Incorporating Critical Flow Conditions
    • R. K. Bhattacharjya, Optimal Design of Open Channel Section Incorporating Critical Flow Conditions, J. Irrigat. Drain. Eng. 2006, 132 (5), 513-518.
    • (2006) J. Irrigat. Drain. Eng. , vol.132 , Issue.5 , pp. 513-518
    • Bhattacharjya, R.K.1
  • 6
    • 0342980419 scopus 로고    scopus 로고
    • Optimal Channel Cross Section with Composite Roughness
    • A. Das, Optimal Channel Cross Section with Composite Roughness, J. Irrigat. Drain. Eng. 2000, 126 (1), 68-72.
    • (2000) J. Irrigat. Drain. Eng. , vol.126 , Issue.1 , pp. 68-72
    • Das, A.1
  • 7
    • 34547117414 scopus 로고    scopus 로고
    • Optimal Design of a Stable Trapezoidal Channel Section Using Hybrid Optimization Techniques
    • R. K. Bhattacharjya, M. G. Satish, Optimal Design of a Stable Trapezoidal Channel Section Using Hybrid Optimization Techniques, J. Irrigat. Drain. Eng. 2007, 133 (4), 323-329.
    • (2007) J. Irrigat. Drain. Eng. , vol.133 , Issue.4 , pp. 323-329
    • Bhattacharjya, R.K.1    Satish, M.G.2
  • 8
    • 33947399164 scopus 로고    scopus 로고
    • Optimal Design of Channel Having Horizontal Bottom and Parabolic Sides
    • A. Das, Optimal Design of Channel Having Horizontal Bottom and Parabolic Sides, J. Irrigat. Drain. Eng. 2007, 133 (2), 192-197.
    • (2007) J. Irrigat. Drain. Eng. , vol.133 , Issue.2 , pp. 192-197
    • Das, A.1
  • 9
    • 33846427333 scopus 로고    scopus 로고
    • Flooding Probability Constrained Optimal Design of Trapezoidal Channels
    • A. Das, Flooding Probability Constrained Optimal Design of Trapezoidal Channels, J. Irrigat. Drain. Eng. 2007, 133 (1), 53-60.
    • (2007) J. Irrigat. Drain. Eng. , vol.133 , Issue.1 , pp. 53-60
    • Das, A.1
  • 10
    • 4143063486 scopus 로고    scopus 로고
    • Optimal Design of Composite Channels Using Genetic Algorithm
    • A. Jain, R. K. Bhattacharjya, S. Sanaga, Optimal Design of Composite Channels Using Genetic Algorithm, J. Irrigat. Drain. Eng. 2004, 130 (4), 286-295.
    • (2004) J. Irrigat. Drain. Eng. , vol.130 , Issue.4 , pp. 286-295
    • Jain, A.1    Bhattacharjya, R.K.2    Sanaga, S.3
  • 11
    • 0023400018 scopus 로고
    • Canal Design: Optimal Cross-sections
    • L. E. Flynn, M. A. Marino, Canal Design: Optimal Cross-sections, J. Irrigat. Drain. Eng. 1987, 113 (3), 335-355.
    • (1987) J. Irrigat. Drain. Eng. , vol.113 , Issue.3 , pp. 335-355
    • Flynn, L.E.1    Marino, M.A.2
  • 12
    • 0028468758 scopus 로고
    • Width and Depth-constrained Best Trapezoidal Section
    • D. C. Froehlich, Width and Depth-constrained Best Trapezoidal Section, J. Irrigat. Drain. Eng. 1994, 120 (4), 828-835.
    • (1994) J. Irrigat. Drain. Eng. , vol.120 , Issue.4 , pp. 828-835
    • Froehlich, D.C.1
  • 13
    • 0021641660 scopus 로고
    • Optimal Channel Cross-section with Freeboard
    • C. Y. Guo, W. C. Hughes, Optimal Channel Cross-section with Freeboard, J. Irrigat. Drain. Eng. 1984, 110 (3), 304-314.
    • (1984) J. Irrigat. Drain. Eng. , vol.110 , Issue.3 , pp. 304-314
    • Guo, C.Y.1    Hughes, W.C.2
  • 14
    • 0026218731 scopus 로고
    • Optimal-design of Parabolic Canals
    • G. V. Loganathan, Optimal-design of Parabolic Canals, J. Irrigat. Drain. Eng. 1991, 117 (5), 716-735.
    • (1991) J. Irrigat. Drain. Eng. , vol.117 , Issue.5 , pp. 716-735
    • Loganathan, G.V.1
  • 16
    • 0028175646 scopus 로고
    • General Formulation of Best Hydraulic Channel Section
    • P. Monadjemi, General Formulation of Best Hydraulic Channel Section, J. Irrigat. Drain. Eng. 1994, 120 (1), 27-35.
    • (1994) J. Irrigat. Drain. Eng. , vol.120 , Issue.1 , pp. 27-35
    • Monadjemi, P.1
  • 17
    • 17144398488 scopus 로고    scopus 로고
    • GIS Tools and the Design of Irrigation Canals
    • H. Depeweg, E. R. Urquieta, GIS Tools and the Design of Irrigation Canals, Irrigat. Drain. 2004, 53 (3), 301-314.
    • (2004) Irrigat. Drain , vol.53 , Issue.3 , pp. 301-314
    • Depeweg, H.1    Urquieta, E.R.2
  • 18
    • 28844434295 scopus 로고    scopus 로고
    • Optimal Design of Parabolic Canal Section
    • B. R. Chahar, Optimal Design of Parabolic Canal Section, J. Irrigat. Drain. Eng. 2005, 131 (6), 546-554.
    • (2005) J. Irrigat. Drain. Eng. , vol.131 , Issue.6 , pp. 546-554
    • Chahar, B.R.1
  • 19
    • 0036428589 scopus 로고    scopus 로고
    • Design of Minimum Waterloss Canal Sections
    • P. K. Swamee, G. C. Mishra, B. R. Chahar, Design of Minimum Waterloss Canal Sections, J. Hydraul. Res. 2002, 40 (2), 215-220.
    • (2002) J. Hydraul. Res. , vol.40 , Issue.2 , pp. 215-220
    • Swamee, P.K.1    Mishra, G.C.2    Chahar, B.R.3
  • 20
    • 0034281938 scopus 로고    scopus 로고
    • Comprehensive Design of Minimum Cost Irrigation Canal Sections
    • P. K. Swamee, G. C. Mishra, B. R. Chahar, Comprehensive Design of Minimum Cost Irrigation Canal Sections, J. Irrigat. Drain. Eng. 2000, 126 (5),322-327.
    • (2000) J. Irrigat. Drain. Eng. , vol.126 , Issue.5 , pp. 322-327
    • Swamee, P.K.1    Mishra, G.C.2    Chahar, B.R.3
  • 21
    • 0037227539 scopus 로고    scopus 로고
    • On the Modeling and Stabilization of Flows in Networks of Open Canals
    • J. Siam, On the Modeling and Stabilization of Flows in Networks of Open Canals, Control Optim. 2002, 41 (1), 164-180.
    • (2002) Control Optim , vol.41 , Issue.1 , pp. 164-180
    • Siam, J.1
  • 23
    • 48449084287 scopus 로고    scopus 로고
    • Most Hydraulically Efficient Standard Lined Canal Sections
    • D. C. Froehlich, Most Hydraulically Efficient Standard Lined Canal Sections, J. Irrigat. Drain. Eng. 2008, 134 (4), 462-470.
    • (2008) J. Irrigat. Drain. Eng. , vol.134 , Issue.4 , pp. 462-470
    • Froehlich, D.C.1
  • 24
    • 33749007673 scopus 로고    scopus 로고
    • Optimal Lined Channel Design
    • B. Aksoy, A. B. Altan-Sakarya, Optimal Lined Channel Design, Can. J. Civ. Eng. 2006, 33 (5), 535-545.
    • (2006) Can. J. Civ. Eng. , vol.33 , Issue.5 , pp. 535-545
    • Aksoy, B.1    Altan-Sakarya, A.B.2
  • 25
    • 0029109674 scopus 로고
    • Kalman Filtering in the Control of Irrigation Canals
    • J. M. Reddy, Kalman Filtering in the Control of Irrigation Canals, Appl. Math. Model. 1995, 19 (4), 201-209.
    • (1995) Appl. Math. Model. , vol.19 , Issue.4 , pp. 201-209
    • Reddy, J.M.1
  • 26
    • 0029395267 scopus 로고
    • Optimal Irrigation Canal Sections
    • P. K. Swamee, Optimal Irrigation Canal Sections, J. Irrigat. Drain. Eng. 1995,121 (6),467-469.
    • (1995) J. Irrigat. Drain. Eng. , vol.121 , Issue.6 , pp. 467-469
    • Swamee, P.K.1
  • 28
    • 0342980419 scopus 로고    scopus 로고
    • Optimal Channel Cross Section with Composite Roughness
    • A. Das, Optimal Channel Cross Section with Composite Roughness, J. Irrigat. Drain. Eng. 2000, 126 (1), 68-72.
    • (2000) J. Irrigat. Drain. Eng. , vol.126 , Issue.1 , pp. 68-72
    • Das, A.1
  • 29
    • 0034079270 scopus 로고    scopus 로고
    • Minimum Cost Design of Lined Canal Sections, Water Resour
    • P. K. Swamee, G. C. Mishra, B. R. Chahar, Minimum Cost Design of Lined Canal Sections, Water Resour. Manage. 2000, 14 (1), 1-12.
    • (2000) Manage. , vol.14 , Issue.1 , pp. 1-12
    • Swamee, P.K.1    Mishra, G.C.2    Chahar, B.R.3
  • 30
    • 0030224949 scopus 로고    scopus 로고
    • Design of Global Control Algorithm for Irrigation Canals
    • J. M. Reddy, Design of Global Control Algorithm for Irrigation Canals, J. Hydraul. Eng. 1996, 122 (9), 503-511.
    • (1996) J. Hydraul. Eng. , vol.122 , Issue.9 , pp. 503-511
    • Reddy, J.M.1
  • 31
    • 70349583350 scopus 로고    scopus 로고
    • Determining Optimal Canal Sections Under Conditions of Constraint
    • V. Federico, Determining Optimal Canal Sections Under Conditions of Constraint, Genio Rurale 1998, 61 (9), 30-35.
    • (1998) Genio Rurale , vol.61 , Issue.9 , pp. 30-35
    • Federico, V.1
  • 33
    • 27244452975 scopus 로고    scopus 로고
    • Artificial Intelligence Methods in Breakwater Damage Ratio Estimation
    • O. Yagci, D. Mercan, H. K. Cigizoglu, S. Kabdasli, Artificial Intelligence Methods in Breakwater Damage Ratio Estimation, Ocean Eng. 2005,32,2088-2106.
    • (2005) Ocean Eng , vol.32 , pp. 2088-2106
    • Yagci, O.1    Mercan, D.2    Cigizoglu, H.K.3    Kabdasli, S.4
  • 34
    • 34547743385 scopus 로고    scopus 로고
    • Deriving Stage-discharge-sediment Concentration Relationships Using Fuzzy Logic
    • A. K. Lohani, N. K. Goel, K. K. S. Bhatia, Deriving Stage-discharge-sediment Concentration Relationships Using Fuzzy Logic, Hydrol. Sci. J. 2007,52 (4),793-807.
    • (2007) Hydrol. Sci. J. , vol.52 , Issue.4 , pp. 793-807
    • Lohani, A.K.1    Goel, N.K.2    Bhatia, K.K.S.3
  • 35
    • 33847207474 scopus 로고    scopus 로고
    • Fuzzy Multiobjective Decision-making Approach for Ground Water Resource Management
    • E. Kentel, M. M. Aral, Fuzzy Multiobjective Decision-making Approach for Ground Water Resource Management, J. Hydrolog. Eng. 2007,12 (2),206-217.
    • (2007) J. Hydrolog. Eng. , vol.12 , Issue.2 , pp. 206-217
    • Kentel, E.1    Aral, M.M.2
  • 36
    • 0034770028 scopus 로고    scopus 로고
    • An Application of Fuzzy Logic to the Assessment of Aquifers' Pollution Potential
    • E. Cameron, G. F. Peloso, An Application of Fuzzy Logic to the Assessment of Aquifers' Pollution Potential, Environ. Geol. 2001, 40 (11-12), 1305-1315.
    • (2001) Environ. Geol. , vol.40 , Issue.11-12 , pp. 1305-1315
    • Cameron, E.1    Peloso, G.F.2
  • 37
    • 24744468053 scopus 로고    scopus 로고
    • Aggregation of Fuzzy Views of a Large Number of Stakeholders for Multi-objective Flood Management Decision-Making
    • T. Akter, S. P. Simonovic, Aggregation of Fuzzy Views of a Large Number of Stakeholders for Multi-objective Flood Management Decision-Making, J. Environ. Manage. 2005, 77 (2), 133-143.
    • (2005) J. Environ. Manage. , vol.77 , Issue.2 , pp. 133-143
    • Akter, T.1    Simonovic, S.P.2
  • 38
    • 78649505998 scopus 로고    scopus 로고
    • Modeling Dispersion in Complex Open Channel Flows: Fuzzy Calibration (2)
    • B. G. Hankin, K. J. Beven, Modeling Dispersion in Complex Open Channel Flows: Fuzzy Calibration (2), Stoch. Hydrol. Hydraul. 1998, 12 (6),377-396.
    • (1998) Stoch. Hydrol. Hydraul. , vol.12 , Issue.6 , pp. 377-396
    • Hankin, B.G.1    Beven, K.J.2
  • 40
    • 61849175296 scopus 로고    scopus 로고
    • Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Intelligence Methods
    • Z. F. Toprak, H. K. Cigizoglu, Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Intelligence Methods, Hydrol. Process. 2008, 22 (20), 4106-4129.
    • (2008) Hydrol. Process. , vol.22 , Issue.20 , pp. 4106-4129
    • Toprak, Z.F.1    Cigizoglu, H.K.2
  • 41
    • 38049071628 scopus 로고    scopus 로고
    • Longitudinal Dispersion Modeling in Natural Channels by Fuzzy Logic
    • Z. F. Toprak, M. E. Savci, Longitudinal Dispersion Modeling in Natural Channels by Fuzzy Logic, Clean: Soil, Air, Water 2007, 35 (6), 626-637.
    • (2007) Clean: Soil, Air, Water , vol.35 , Issue.6 , pp. 626-637
    • Toprak, Z.F.1    Savci, M.E.2
  • 44
    • 10944222907 scopus 로고    scopus 로고
    • Determination of Fuzzy Logic Membership Functions Using Genetic Algorithms: Application to Structure-odor Modeling
    • M. Kissi, M. Ramdani, M. Tollabi, D. Zakarya, Determination of Fuzzy Logic Membership Functions Using Genetic Algorithms: Application to Structure-odor Modeling, J. Mol. Model. 2004, 10 (5-6), 335-341.
    • (2004) J. Mol. Model. , vol.10 , Issue.5-6 , pp. 335-341
    • Kissi, M.1    Ramdani, M.2    Tollabi, M.3    Zakarya, D.4
  • 45
    • 0032178733 scopus 로고    scopus 로고
    • Genetic Algorithm Simulation Approach to Determine Membership Functions of Fuzzy Traffic Controller
    • J. W. Kim, B. M. Kim, J. Y. Kim, Genetic Algorithm Simulation Approach to Determine Membership Functions of Fuzzy Traffic Controller, Electron. Lett. 1998, 34 (20), 1982-1983.
    • (1998) Electron. Lett. , vol.34 , Issue.20 , pp. 1982-1983
    • Kim, J.W.1    Kim, B.M.2    Kim, J.Y.3
  • 46
    • 0036924127 scopus 로고    scopus 로고
    • Automatically Constructing Membership Functions and Generating Fuzzy Rules Using Genetic Algorithms
    • S. M. Chen, Y. C. Chen, Automatically Constructing Membership Functions and Generating Fuzzy Rules Using Genetic Algorithms, Cybern. Syst. 2002, 33 (8), 841-862.
    • (2002) Cybern. Syst. , vol.33 , Issue.8 , pp. 841-862
    • Chen, S.M.1    Chen, Y.C.2
  • 47
    • 0033722784 scopus 로고    scopus 로고
    • A Genetic Approach for Simultaneous Design of Membership Functions and Fuzzy Control Rules
    • C.-J. Wu, G.-Y. Liu, A Genetic Approach for Simultaneous Design of Membership Functions and Fuzzy Control Rules, J. Intell. Robotic Syst. 2000,28,195-211.
    • (2000) J. Intell. Robotic Syst. , vol.28 , pp. 195-211
    • Wu, C.-J.1    Liu, G.-Y.2
  • 48
    • 70349592231 scopus 로고    scopus 로고
    • Parallel Genetic Evolution of Membership Functions and Rules for a Fuzzy Controller, in High-Performance Computing And Networking
    • (Eds.: P. Sloot, M. Bubak, B. Hertzberger), Springer, Berlin
    • G. Mondelli, G. Castellano, G. Attolico, C. Distante, Parallel Genetic Evolution of Membership Functions and Rules for a Fuzzy Controller, in High-Performance Computing And Networking (Eds.: P. Sloot, M. Bubak, B. Hertzberger), Lecture Notes in Computer Science, Vol. 1401, Springer, Berlin 1998, pp. 922-924.
    • (1998) Lecture Notes in Computer Science , vol.1401 , pp. 922-924
    • Mondelli, G.1    Castellano, G.2    Attolico, G.3    Distante, C.4
  • 49
    • 0035415951 scopus 로고    scopus 로고
    • A Fast Approach for Automatic Generation of Fuzzy Rules by Generalized Dynamic Fuzzy Neural Networks
    • S. Q. Wu, M. J. Er, Y. Gao, A Fast Approach for Automatic Generation of Fuzzy Rules by Generalized Dynamic Fuzzy Neural Networks, IEEE Trans. Fuzzy Syst. 2001, 9 (4), 578-594.
    • (2001) IEEE Trans. Fuzzy Syst. , vol.9 , Issue.4 , pp. 578-594
    • Wu, S.Q.1    Er, M.J.2    Gao, Y.3
  • 50
    • 22044452705 scopus 로고    scopus 로고
    • Automatic Generation of Fuzzy Rules Using Hyper-elliptic-cone Membership Functions by Genetic Algorithms
    • H. Inoue, K. Kamei, K. Inoue, Automatic Generation of Fuzzy Rules Using Hyper-elliptic-cone Membership Functions by Genetic Algorithms, J. Intell. Fuzzy Syst. 1998, 6 (1), 65-81.
    • (1998) J. Intell. Fuzzy Syst. , vol.6 , Issue.1 , pp. 65-81
    • Inoue, H.1    Kamei, K.2    Inoue, K.3
  • 51
    • 7444252803 scopus 로고    scopus 로고
    • Optimization of Fuzzy Rules for Classification Using Genetic Algorithm, in Advances in Knowledge Discovery and Data Mining
    • (Eds.: U. M. Fayyad, G. Piatetsky-Shapiro, R., Uthurusami), Springer, Berlin
    • M. W. Kim, J. W. Ryu, S. Kim, J. G. Lee, Optimization of Fuzzy Rules for Classification Using Genetic Algorithm, in Advances in Knowledge Discovery and Data Mining (Eds.: U. M. Fayyad, G. Piatetsky-Shapiro, R. Uthurusami), Lecture Notes in Artificial Intelligence, Vol. 2637, Springer, Berlin 2003, pp. 363-375.
    • (2003) Lecture Notes in Artificial Intelligence , vol.2637 , pp. 363-375
    • Kim, M.W.1    Ryu, J.W.2    Kim, S.3    Lee, J.G.4
  • 52
    • 0038731039 scopus 로고    scopus 로고
    • Learning Fuzzy Rules for Controllers with Genetic Algorithms
    • T. Pal, N. R. Pal, M. Pal, Learning Fuzzy Rules for Controllers with Genetic Algorithms, Int. J. Intell. Syst. 2003, 18 (5), 569-592.
    • (2003) Int. J. Intell. Syst. , vol.18 , Issue.5 , pp. 569-592
    • Pal, T.1    Pal, N.R.2    Pal, M.3
  • 53
    • 35048848055 scopus 로고    scopus 로고
    • A Study of the Reasoning Methods Impact on Genetic Learning and Optimization of Fuzzy Rules, in Advances in Artificial Intelligence
    • (Eds.: A. Y. Tawfik, S. D. Goodwin), Springer, Berlin
    • P. A. de Castro, H. A. Camargo, A Study of the Reasoning Methods Impact on Genetic Learning and Optimization of Fuzzy Rules, in Advances in Artificial Intelligence (Eds.: A. Y. Tawfik, S. D. Goodwin), Lecture Notes in Artificial Intelligence, Vol. 3171, Springer, Berlin 2004, pp. 414-423.
    • (2004) Lecture Notes in Artificial Intelligence , vol.3171 , pp. 414-423
    • de Castro, P.A.1    Camargo, H.A.2
  • 54
    • 0032090022 scopus 로고    scopus 로고
    • On Generating Fuzzy Rules by an Evolutionary Approach
    • D. Rutkowska, On Generating Fuzzy Rules by an Evolutionary Approach, Cybern. Syst. 1998, 29 (4), 391-407.
    • (1998) Cybern. Syst. , vol.29 , Issue.4 , pp. 391-407
    • Rutkowska, D.1
  • 55
    • 17644415734 scopus 로고    scopus 로고
    • An Asymmetry-similarity-measure-based Neural Fuzzy Inference System, Fuzzy Set
    • C. J. Lin, W. H. Ho, An Asymmetry-similarity-measure-based Neural Fuzzy Inference System, Fuzzy Set. Syst. 2005, 152 (3), 535-551.
    • (2005) Syst. , vol.152 , Issue.3 , pp. 535-551
    • Lin, C.J.1    Ho, W.H.2
  • 56
    • 26844480403 scopus 로고    scopus 로고
    • Estimating Membership Functions in a Fuzzy Network Model for Part-of-speech Tagging
    • J. H. Kim, J. Y. Seo, G. C. Kim, Estimating Membership Functions in a Fuzzy Network Model for Part-of-speech Tagging, J. Intell. Fuzzy Syst. 1996,4 (4),309-320.
    • (1996) J. Intell. Fuzzy Syst. , vol.4 , Issue.4 , pp. 309-320
    • Kim, J.H.1    Seo, J.Y.2    Kim, G.C.3
  • 57
    • 11244351634 scopus 로고    scopus 로고
    • An Approach for Online Extraction of Fuzzy Rules Using a Self-Organizing Fuzzy Neural Network
    • G. Leng, T. M. McGinnity, G. Prasad, An Approach for On-line Extraction of Fuzzy Rules Using a Self-Organizing Fuzzy Neural Network, Fuzzy Set. Syst. 2005, 150 (2), 211-243.
    • (2005) Fuzzy Set. Syst. , vol.150 , Issue.2 , pp. 211-243
    • Leng, G.1    McGinnity, T.M.2    Prasad, G.3
  • 58
    • 70349587776 scopus 로고    scopus 로고
    • Extraction of Fuzzy Rules Using Sensibility Analysis in a Neural Network, in Artificial Neural Networks - ICANN 2002
    • (Ed.: J. R. Dorronsoro), Springer, Berlin
    • J. M. Besada-Juez, M. A. Sanz-Bobi, Extraction of Fuzzy Rules Using Sensibility Analysis in a Neural Network, in Artificial Neural Networks - ICANN 2002 (Ed.: J. R. Dorronsoro), Lecture Notes in Computer Science, Vol. 2415, Springer, Berlin 2002, pp. 395-400.
    • (2002) Lecture Notes in Computer Science , vol.2415 , pp. 395-400
    • Besada-Juez, J.M.1    Sanz-Bobi, M.A.2
  • 59
    • 0038173250 scopus 로고    scopus 로고
    • Extracting Interpretable Fuzzy Rules from RBF Networks
    • Y. C. Jin, B. Sendhoff, Extracting Interpretable Fuzzy Rules from RBF Networks, Neural Process. Lett. 2003, 17 (2), 149-164.
    • (2003) Neural Process. Lett. , vol.17 , Issue.2 , pp. 149-164
    • Jin, Y.C.1    Sendhoff, B.2
  • 60
    • 0036693605 scopus 로고    scopus 로고
    • Sum Normal Optimization of Fuzzy Membership Functions
    • D. Simon, Sum Normal Optimization of Fuzzy Membership Functions, Int. J. Uncertainty Fuzziness Knowledge-Based Syst. 2002, 10 (4), 363-384.
    • (2002) Int. J. Uncertainty Fuzziness Knowledge-Based Syst. , vol.10 , Issue.4 , pp. 363-384
    • Simon, D.1
  • 61
    • 4944249799 scopus 로고    scopus 로고
    • Membership Functions and Probability Measures of Fuzzy Sets
    • N. D. Singpurwalla, J. M. Booker, Membership Functions and Probability Measures of Fuzzy Sets, J. Am. Stat. Assoc. 2004, 99 (467), 867-877.
    • (2004) J. Am. Stat. Assoc. , vol.99 , Issue.467 , pp. 867-877
    • Singpurwalla, N.D.1    Booker, J.M.2
  • 62
    • 4944255166 scopus 로고    scopus 로고
    • Membership Functions and Probability Measures of Fuzzy Sets: Comment
    • A. P. Dempster, Membership Functions and Probability Measures of Fuzzy Sets: Comment, J. Am. Stat. Assoc. 2004, 99 (467), 882-884.
    • (2004) J. Am. Stat. Assoc. , vol.99 , Issue.467 , pp. 882-884
    • Dempster, A.P.1
  • 63
    • 4944255166 scopus 로고    scopus 로고
    • Membership Functions and Probability Measures of Fuzzy Sets: Comment
    • L. A. Zadeh, Membership Functions and Probability Measures of Fuzzy Sets: Comment, J. Am. Stat. Assoc. 2004, 99 (467), 880-881.
    • (2004) J. Am. Stat. Assoc. , vol.99 , Issue.467 , pp. 880-881
    • Zadeh, L.A.1
  • 64
    • 4944255166 scopus 로고    scopus 로고
    • Membership Functions and Probability Measures of Fuzzy Sets: Comment
    • D. V. Lindley, Membership Functions and Probability Measures of Fuzzy Sets: Comment, J. Am. Stat. Assoc. 2004, 99 (467), 877-879.
    • (2004) J. Am. Stat. Assoc. , vol.99 , Issue.467 , pp. 877-879
    • Lindley, D.V.1
  • 65
    • 4944255166 scopus 로고    scopus 로고
    • Membership Functions and Probability Measures of Fuzzy Sets: Comment
    • M. Laviolette, Membership Functions and Probability Measures of Fuzzy Sets: Comment, J. Am. Stat. Assoc. 2004, 99 (467), 879-880.
    • (2004) J. Am. Stat. Assoc. , vol.99 , Issue.467 , pp. 879-880
    • Laviolette, M.1
  • 66
    • 4944249799 scopus 로고    scopus 로고
    • Membership Functions and Probability Measures of Fuzzy Sets: Rejoinder
    • N. D. Singpurwalla, J. M. Booker, Membership Functions and Probability Measures of Fuzzy Sets: Rejoinder, J. Am. Stat. Assoc. 2004, 99 (467),884-889.
    • (2004) J. Am. Stat. Assoc. , vol.99 , Issue.467 , pp. 884-889
    • Singpurwalla, N.D.1    Booker, J.M.2
  • 67
    • 0033323230 scopus 로고    scopus 로고
    • Methods for the Construction of Membership Functions
    • A. Sancho-Royo, J. L. Verdegay, Methods for the Construction of Membership Functions, Int. J. Intell. Syst. 1999, 14 (12), 1213-1230.
    • (1999) Int. J. Intell. Syst. , vol.14 , Issue.12 , pp. 1213-1230
    • Sancho-Royo, A.1    Verdegay, J.L.2
  • 68
    • 0029639613 scopus 로고
    • Constructing Membership Functions Using Interpolation and Measurement Theory
    • J. E. Chen, K. N. Otto, Constructing Membership Functions Using Interpolation and Measurement Theory, Fuzzy Set. Syst. 1995, 73 (3), 313-327.
    • (1995) Fuzzy Set. Syst. , vol.73 , Issue.3 , pp. 313-327
    • Chen, J.E.1    Otto, K.N.2
  • 69
    • 0011436837 scopus 로고
    • An Alternative Approach for Generation of Membership Functions and Fuzzy Rules Based on Radial and Cubic Basis Function Networks
    • S. K. Halgamuge, W. Poechmueller, M. Glesner, An Alternative Approach for Generation of Membership Functions and Fuzzy Rules Based on Radial and Cubic Basis Function Networks, Int. J. Approx. Reasoning 1995, 12 (3-4), 279-298.
    • (1995) Int. J. Approx. Reasoning , vol.12 , Issue.3-4 , pp. 279-298
    • Halgamuge, S.K.1    Poechmueller, W.2    Glesner, M.3
  • 70
    • 0033078158 scopus 로고    scopus 로고
    • A New Method for Constructing Membership Functions and Fuzzy Rules from Training Examples
    • T. P. Wu, S. M. Chen, A New Method for Constructing Membership Functions and Fuzzy Rules from Training Examples, IEEE Trans. Syst. Man. Cybern., B: Cybern. 1999, 29 (1), 25-40.
    • (1999) IEEE Trans. Syst. Man. Cybern., B: Cybern. , vol.29 , Issue.1 , pp. 25-40
    • Wu, T.P.1    Chen, S.M.2
  • 71
    • 0032155391 scopus 로고    scopus 로고
    • Autogeneration of Fuzzy Rules and Membership Functions for Fuzzy Modeling Using Rough Set Theory
    • Y. Cho, K. Lee, J. Yoo, M. Park, Autogeneration of Fuzzy Rules and Membership Functions for Fuzzy Modeling Using Rough Set Theory, IEE Proc. Control Theor. Appl. 1998, 145 (5), 437-442.
    • (1998) IEE Proc. Control Theor. Appl. , vol.145 , Issue.5 , pp. 437-442
    • Cho, Y.1    Lee, K.2    Yoo, J.3    Park, M.4
  • 72
    • 0030289773 scopus 로고    scopus 로고
    • Induction of Fuzzy Rules and Membership Functions from Training
    • T. P. Hong, C. Y. Lee, Induction of Fuzzy Rules and Membership Functions from Training Examples, Fuzzy Set. Syst. 1996, 84 (1), 33-47.
    • (1996) Examples, Fuzzy Set. Syst. , vol.84 , Issue.1 , pp. 33-47
    • Hong, T.P.1    Lee, C.Y.2
  • 74
    • 1942500885 scopus 로고    scopus 로고
    • A Characteristic-point-based Fuzzy Inference System Aimed to Minimize the Number of Fuzzy Rules
    • T. K. Yin, A Characteristic-point-based Fuzzy Inference System Aimed to Minimize the Number of Fuzzy Rules, IEEE Trans. Fuzzy Syst. 2004, 12 (2),250-273.
    • (2004) IEEE Trans. Fuzzy Syst. , vol.12 , Issue.2 , pp. 250-273
    • Yin, T.K.1
  • 75
    • 9144258054 scopus 로고    scopus 로고
    • A New Method to Generate Fuzzy Rules from Training Instances for Handling Classification Problems
    • S. M. Chen, C. H. Yu, A New Method to Generate Fuzzy Rules from Training Instances for Handling Classification Problems, Cybern. Syst. 2003,34 (3),217-232.
    • (2003) Cybern. Syst. , vol.34 , Issue.3 , pp. 217-232
    • Chen, S.M.1    Yu, C.H.2
  • 76
    • 0032026149 scopus 로고    scopus 로고
    • Algorithm for Automatic Generation of Fuzzy Rules Applied to Power System Controllers
    • A. M. Luciano, D. Lauria, E. Napoli, Algorithm for Automatic Generation of Fuzzy Rules Applied to Power System Controllers, IEE Proc. Generat. Transm. Distrib. 1998, 145 (2), 161-167.
    • (1998) IEE Proc. Generat. Transm. Distrib. , vol.145 , Issue.2 , pp. 161-167
    • Luciano, A.M.1    Lauria, D.2    Napoli, E.3
  • 77
    • 0036795586 scopus 로고    scopus 로고
    • Generating Fuzzy Rules from Training Data Containing Noise for Handling Classification Problem
    • S. M. Chen, C. H. Kao, C. H. Yu, Generating Fuzzy Rules from Training Data Containing Noise for Handling Classification Problem, Cybern. Syst. 2002, 33 (7), 723-748.
    • (2002) Cybern. Syst. , vol.33 , Issue.7 , pp. 723-748
    • Chen, S.M.1    Kao, C.H.2    Yu, C.H.3
  • 78
    • 11244305514 scopus 로고    scopus 로고
    • How to Determine the Minimum Number of Fuzzy Rules to Achieve Given Accuracy: A Computational Geometric Approach to SISO Case
    • F. Wan, H. L. Shang, L. X. Wang, Y. X. Sun, How to Determine the Minimum Number of Fuzzy Rules to Achieve Given Accuracy: A Computational Geometric Approach to SISO Case, Fuzzy Set. Syst. 2005, 150 (2),199-209.
    • (2005) Fuzzy Set. Syst. , vol.150 , Issue.2 , pp. 199-209
    • Wan, F.1    Shang, H.L.2    Wang, L.X.3    Sun, Y.X.4
  • 79
    • 17644387146 scopus 로고    scopus 로고
    • Learning Cooperative Linguistic Fuzzy Rules Using the Best-Worst Ant System Algorithm
    • J. Casillas, O. Cordon, I. F. de Viana, F. Herrera, Learning Cooperative Linguistic Fuzzy Rules Using the Best-Worst Ant System Algorithm, Int. J. Intell. Syst. 2005, 20 (4), 433-452.
    • (2005) Int. J. Intell. Syst. , vol.20 , Issue.4 , pp. 433-452
    • Casillas, J.1    Cordon, O.2    de Viana, I.F.3    Herrera, F.4
  • 80
    • 0033242063 scopus 로고    scopus 로고
    • Learning Fuzzy Rules from Data
    • G. D. Finn, Learning Fuzzy Rules from Data, Neural Comput. Appl. 1999,8 (1),9-24.
    • (1999) Neural Comput. Appl. , vol.8 , Issue.1 , pp. 9-24
    • Finn, G.D.1
  • 81
    • 85011485078 scopus 로고    scopus 로고
    • A New Method to Generate Fuzzy Rules from Relational Database Systems for Estimating Null Values
    • S. M. Chen, S. W. Lee, A New Method to Generate Fuzzy Rules from Relational Database Systems for Estimating Null Values, Cybern. Syst. 2003,34 (1),33-57.
    • (2003) Cybern. Syst. , vol.34 , Issue.1 , pp. 33-57
    • Chen, S.M.1    Lee, S.W.2
  • 82
    • 0033317885 scopus 로고    scopus 로고
    • Fuzzy Rules Generation Using new Evolutionary Algorithms Combined with Multilayer Perceptrons
    • C. S. Fahn, K. T. Lan, Z. B. Chern, Fuzzy Rules Generation Using new Evolutionary Algorithms Combined with Multilayer Perceptrons, IEEE Trans. Ind. Electron. 1999, 46 (6), 1103-1113.
    • (1999) IEEE Trans. Ind. Electron. , vol.46 , Issue.6 , pp. 1103-1113
    • Fahn, C.S.1    Lan, K.T.2    Chern, Z.B.3
  • 83
    • 45449126257 scopus 로고    scopus 로고
    • Structure Identification of Fuzzy Model
    • M. Sugeno, G. T. Kank, Structure Identification of Fuzzy Model, Fuzzy Set. Syst. 1998, 28 (1), 15-33.
    • (1998) Fuzzy Set. Syst. , vol.28 , Issue.1 , pp. 15-33
    • Sugeno, M.1    Kank, G.T.2
  • 84
    • 0017703889 scopus 로고
    • Applications of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
    • E. H. Mamdani, Applications of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis, IEEE Trans. Comput. 1977, 26 (12), 1182-1191.
    • (1977) IEEE Trans. Comput. , vol.26 , Issue.12 , pp. 1182-1191
    • Mamdani, E.H.1


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