Building fuzzy graphs: Features and taxonomy of learning for non-grid-oriented fuzzy rule-based systems

Journal of Intelligent and Fuzzy Systems

Volumn 11, Issue 3-4, 2001, Pages 99-119

  Alcalá, Rafael ^a   Casillas, Jorge ^b   Cordón, Oscar ^b   Herrera, Francisco ^b  

^a UNIVERSITY OF JAÉN   (Spain)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

Accuracy improvement; Fuzzy graphs; Fuzzy modeling; Learning; Non grid oriented fuzzy rule based systems

Indexed keywords

EID: 0345876941     PISSN: 10641246     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (103)

1. R. Alcalá, J. Casillas, O. Cordón and F. Herrera, Cooperative coevolution for linguistic modeling with weighted double-consequent fuzzy rules, in: Proceedings of the 2nd International Conference in Fuzzy Logic and Technology, Leicester, UK, 2001, pp. 237-240.
2. R. Babuška, Fuzzy modeling for control, Kluwer Academic, Norwell, MA, USA, 1998.
3. T. Bäck, ed., Evolutionary algorithms in theory and practice, Oxford University Press, UK, 1996.
4. J.E. Baker, Reducing bias and inefficiency in the selection algorithm, in: Proceedings of the 2nd International Conference on Genetic Algorithms, Hillsdale, NJ, USA, 1987, Lawrence Erlbaum, pp. 14-21.
5. A. Bárdossy and L. Duckstein, Fuzzy rule-based modeling with application to geophysical, biological and engineering systems, CRC Press, Boca Raton, FL, USA, 1995.
6. A. Bastian, How to handle the exibility of linguistic variables with applications, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2(4) (1994), 463-484.
7. M.R. Berthold and K.-P. Huber, Constructing fuzzy graphs from examples, Intelligent Data Analysis 3 (1999), 37-53.
8. J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, 1981.
9. M. Bikdash, A highly interpretable form of Sugeno inference systems, IEEE Transactions on Fuzzy Systems 7(6) (1999), 686-696.
10. P.P. Bonissone, Soft computing: the convergence of emerging reasoning technologies. Soft Computing, A Fusion of Foundations, Methodologies, and Applications 1(1) (1997), 6-18.
11. P.P. Bonissone, P.S. Khedkar and Y. Chen, Genetic algorithms for automated tuning of fuzzy controllers: a transportation application, in: Proceedings of the 5th IEEE International Conference on Fuzzy Systems, New Orleans, LA, USA, 1996, pp. 674-680.
12. B. Carse, T.C. Fogarty and A. Munro, Evolving fuzzy rule based controllers using genetic algorithms, Fuzzy Sets and Systems 80 (1996), 273-294.
13. J. Casillas, O. Cordón, M.J. del Jesus and F. Herrera, Genetic feature selection in a fuzzy rule-based classification system learning process for high dimensional problems, Information Sciences 136(1-4) (2001), 169-191.
14. J. Casillas, O. Cordón, M.J. del Jesus and F. Herrera, Genetic tuning of fuzzy rule-based systems integrating linguistic hedges, in: Proceedings of the 9th IFSA World Congress and the 20th NAFIPS International Conference, Vancouver, Canada, 2001, pp. 1570-1574.
15. J. Casillas, O. Cordon, F. Herrera and L. Magdalena, eds, Fuzzy modeling and the interpretability - accuracy trade-off. Part I, interpretability issues. Part II, accuracy improvements preserving the interpretability. Physica-Verlag, Heidelberg, Germany, 2002, in press.
16. S.L. Chiu, Fuzzy model identification based on cluster estimation, Journal of Intelligent and Fuzzy Systems 2 (1994), 267-278.
17. J.-S. Cho and D.-J. Park, Novel fuzzy logic control based on weighting of partially inconsistent rules using neurtal network, Journal of Intelligent and Fuzzy Systems 8 (2000), 99-110.
18. W.E. Combs and J.E. Andrews, Combinatorial rule explosion eliminated by a fuzzy rule configuration, IEEE Transactions on Fuzzy Systems 6(1) (1998), 1-11.
19. M.G. Cooper and J.J. Vidal, Genetic design of fuzzy controllers: the cart and jointed pole problem, in: Proceedings of the 3rd IEEE International Conference on Fuzzy Systems, Piscataway, NJ, USA, 1994, pp. 1332-1337.
20. O. Cordón, M.J. del Jesus and F. Herrera, Genetic learning of fuzzy rule-based classification systems cooperating with fuzzy reasoning methods, International Journal of Intelligent Systems 13 (1998), 1025-1053.
21. O. Cordón, M.J. del Jesus, F. Herrera and M. Lozano, MOGUL: A methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning, International Journal of Intelligent Systems 14(11) (1999), 1123-1153.
22. O. Cordón and F. Herrera, A general study on genetic fuzzy systems, in: Genetic algorithms in engineering and computer science, J. Periaux, G. Winter, M. Galán and P. Cuesta, eds, Wiley, New York, USA, 1995, pp. 33-57.
23. O. Cordón and F. Herrera, A hybrid genetic algorithm-evolution strategy process for learning fuzzy logic controller knowledge bases, in: Fuzzy logic and soft computing, F. Herrera and J.L. Verdegay, eds, Physica-Verlag, Heidelberg, Germany, 1996, pp. 251-278.
24. O. Cordón and F. Herrera, A three-stage evolutionary process for learning descriptive and approximate fuzzy logic controller knowledge bases from examples, International Journal of Approximate Reasoning 17(4) (1997), 369-407.
25. O. Cordón and F. Herrera, A two-stage evolutionary process for designing TSK fuzzy rule-based systems, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 29(6) (1999), 703-715.
26. O. Cordón and F. Herrera, A proposal for improving the accuracy of linguistic modeling, IEEE Transactions on Fuzzy Systems 8(3) (2000), 335-344.
27. O. Cordón and F. Herrera, Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems, Fuzzy Sets and Systems 118(2) (2001), 235-255.
28. O. Cordón, F. Herrera, F. Hoffmann and L. Magdalena, Genetic fuzzy systems: evolu-tionary tuning and learning of fuzzy knowledge bases, World Scientific, Singapore, 2001.
29. O. Cordón, F. Herrera, L. Magdalena and P. Villar, A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base, Information Sciences 136(1-4) (2001), 85-107.
30. O. Cordón, F. Herrera and A. Peregrín, Applicability of the fuzzy operators in the design of fuzzy logic controllers, Fuzzy Sets and Systems 86(1) (1997), 15-41.
31. O. Cordón, F. Herrera and L. Sánchez, Solving electrical distribution problems using hybrid evolutionary data analysis techniques, Applied Intelligence 10(1) (1999), 5-24.
32. O. Cordón, F. Herrera and P. Villar, Analysis and guidelines to obtain a good fuzzy partition granularity for fuzzy rule-based systems using simulated annealing, International Journal of Approximate Reasoning 25(3) (2000), 187-215.
33. O. Cordón, F. Herrera and I. Zwir, Linguistic modeling by hierarchical systems of linguistic rules, IEEE Transactions on Fuzzy Systems 10(1) (2002), 1-20.
34. J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters, Journal Cybernetics 3(3) (1974), 32-57.
35. A. Dvořák, On linguistic approximation in the frame of fuzzy logic deduction, Soft Computing 3(2) (1999), 111-116.
36. J. Espinosa and J. Vandewalle, Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm, IEEE Transactions on Fuzzy Systems 8(5) (2000), 591-600.
37. A. Fiordaliso, Autostructuration of fuzzy systems by rules sensitivity analysis, Fuzzy Sets and Systems 118(2) (2001), 281-296.
38. A. Fiordaliso, A constrained Takagi-Sugeno fuzzy system that allows for better interpretation and analysis, Fuzzy Sets and Systems 118(2) (2001), 307-318.
39. B. Fritzke, Incremental neuro-fuzzy systems, in: Proceedings of the International Society for Optical Engineering: Applications of Soft Computing, (Vol. 3165), B. Bosacchi, J.C. Bezdek and D.B. Fogel, eds, 1997, pp. 86-97.
40. A. González and R. Pérez, SLAVE: a genetic learning system based on an iterative approach, IEEE Transactions on Fuzzy Systems 7(2) (1999), 176-191.
41. A. González and R. Pérez, A study about the inclusion of linguistic hedges in a fuzzy rule learning algorithm, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 7(3) (1999), 257-266.
42. A. González and R. Pérez, Selection of relevant features in a fuzzy genetic learning algorithm, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 31(3) (2001), 417-425.
43. J.J. Grefensette, ed., Genetic algorithms for machine learning, Kluwer Academic, Norwell, MA, USA, 1994.
44. R.R. Gudwin and F.A.C. Gomide, Context adaptation in fuzzy processing, in: Proceedings of the 1st Brazil-Japan Joint Symposium in Fuzzy Systems, Campinas, SP, Brazil, 1994, pp. 15-20.
45. R.R. Gudwin, F.A.C. Gomide and W. Pedrycz, Context adaptation in fuzzy processing and genetic algorithms, International Journal of Intelligent Systems 13(10-11) (1998), 929-948.
46. F. Herrera, ed., Special issue on genetic fuzzy systems for control and robotics, International Journal of Approximate Reasoning 17(4) (1997).
47. F. Herrera, M. Lozano and J.L. Verdegay, Tuning fuzzy controllers by genetic algorithms, International Journal of Approximate Reasoning 12 (1995), 299-315.
48. F. Herrera, M. Lozano and J.L. Verdegay, Fuzzy connectives based crossover operators to model genetic algorithm population diversity, Fuzzy Sets and Systems 92(1) (1997), 21-30.
49. F. Herrera, M. Lozano and J.L. Verdegay, A learning process for fuzzy control rules using genetic algorithms, Fuzzy Sets and Systems 100 (1998), 143-158.
50. F. Herrera and L. Magdalena, eds, Special issue on genetic fuzzy systems, International Journal of Intelligent Systems 13(10) (1998).
51. F. Herrera and J.L. Verdegay, eds, Genetic algorithms and soft computing, Physica-Verlag, Heidelberg, Germany, 1996.
52. J.H. Holland and J.S. Reitman, Cognitive systems based on adaptive algorithms, in: Pattern-directed inference systems, D.A. Waterman and F. Hayes-Roth, eds, Academic Press, San Diego, USA, 1978, pp. 313-329.
53. A. Homaifar and E. McCormick, Simultaneous design of membership functions and rule sets for fuzzy controllers us-ing genetic algoritms, IEEE Transactions on Fuzzy Systems 3(2) (1995), 129-139.
54. T.-P. Hong and J.-B. Chen, Finding relevant attributes and membership functions, Fuzzy Sets and Systems 103(3) (1999), 389-404.
55. T.-P. Hong and C.-Y. Lee, Effect of merging order on performance of fuzzy induction, Intelligent Data Analysis 3(2) (1999), 139-151.
56. H. Ishibuchi, T. Murata and I.B. Türkşen, Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems, Fuzzy Sets and Systems 89(2) (1997), 135-150.
57. H. Ishibuchi and T. Nakashima, Effect of rule weights in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems 9(4) (2001), 506-515.
58. H. Ishibuchi, K. Nozaki, N. Yamamoto and H. Tanaka, Selecting fuzzy if-then rules for classification problems using genetic algorithms, IEEE Transactions on Fuzzy Systems 3(3) (1995), 260-270.
59. Y. Jin, Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement, IEEE Transactions on Fuzzy Systems 8(2) (2000), 212-221.
60. Y. Jin, W. von Seelen and B. Sendhoff, On generating FC 3 fuzzy rule systems from data using evolution strategies, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 29(4) (1999), 829-845.
61. C.L. Karr, Genetic algorithms for fuzzy controllers, AI Expert 6(2) (1991), 26-33.
62. L. Koczy, Fuzzy if . . . then rule models and their transformation one another, IEEE Transactions on Systems, Man, and Cybernetics 26(5) (1996), 621-637.
63. A. Krone and H. Taeger, Data-based fuzzy rule test for fuzzy modelling, Fuzzy Sets and Systems 123(3) (2001), 343-358.
64. H.-M. Lee, C.-M. Chen, J.-M. Chen and Y.-L. Jou, An efficient fuzzy classifier with feature selection based on fuzzy entropy, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 31(3) (2001), 426-432.
65. J. Liska and S.S. Melsheimer, Complete design of fuzzy logic systems using genetic algorithms, in: Proceedings of the 3rd IEEE International Conference on Fuzzy Systems, Orlando, FL, USA, 1994, pp. 1377-1382.
66. B.-D. Liu, C.-Y. Chen and J.-Y Tsao, Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 31(1) (2001), 32-53.
67. L. Magdalena, Adapting the gain of an FLC with genetic algorithms, International Journal of Approximate Reasoning 17(4) (1997), 327-349.
68. L. Magdalena and F. Monasterio-Huelin, A fuzzy logic controller with learning through the evolution of its knowledge base, International Journal of Approximate Reasoning 16(3) (1997), 335-358.
69. E.H. Mamdani, Applications of fuzzy algorithms for control a simple dynamic plant, Proceedings of the IEE 121(12) (1974), 1585-1588.
70. E.H. Mamdani and S. Assilian , An experiment in linguistic systhesis with fuzzy logic controller, International Journal of Man-Machine Studies 7 (1975), 1-13.
71. J.G. Marín-Blázquez, Q. Shen and A.F. Gómez-Skarmeta, From approximative to descriptive models, in: Proceedings of the 9th IEEE International Conference on Fuzzy Systems, San Antonio, TX, USA, 2000, pp. 829-834.
72. Z. Michalewicz, Genetic algorithms + data structures = evolution programs, (3rd ed.), Springer-Verlag, Heidelberg, Germany, 1996.
73. G.C. Mouzouris and J.M. Mendel, A singular-value-QR decomposition based method for training fuzzy logic systems in uncertain environments, Journal of Intelligent and Fuzzy Systems 5 (1997), 367-374.
74. D. Nauck and R. Kruse, Neuro-fuzzy systems for function approximaton, Fuzzy Sets and Systems 101(2) (1999), 261-271.
75. N.R. Pal and K. Pal, Handling of inconsistent rules with an extended model of fuzzy reasoning, Journal of Intelligent and Fuzzy Systems 7 (1999), 55-73.
76. W. Pedrycz, ed., Fuzzy modelling: paradigms and practice, Kluwer Academic, Norwell, MA, USA, 1996.
77. W. Pedrycz, R.R. Gudwin and F.A.C. Gomide, Nonlinear context adaptation in the calibration of fuzzy sets, Fuzzy Sets and Systems 88(1) (1997), 91-97.
78. H. Pomares, I. Rojas, J. Ortega, J. González and A. Prieto, A systematic approach to a self-generating fuzzy rule-table for function approximation, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 30(3) (2000), 431-447.
79. I. Rojas, H. Pomares, J. Ortega and A. Prieto, Self-organized fuzzy system generation from training examples, IEEE Transactions on Fuzzy Systems 8(1) (2000), 23-36.
80. H. Roubos and M. Setnes, Compact and transparent fuzzy models and classifiers through iterative complexity reduction, IEEE Transactions on Fuzzy Systems 9(4) (2001), 516-524.
81. L. Sánchez, J. Casillas, O. Cordón and M.J. del Jesus, Some relationships between fuzzy and random set-based classifiers and models, International Journal of Approximate Reasoning 29(2) (2002), 175-213.
82. M. Setnes, R. Babuška, U. Kaymak and H.R. van Nauta Lemke, Similarity measures in fuzzy rule base simplificá tion, IEEE Transactions on Systems, Man, and Cybernetics -Part B: Cybernetics 28(3) (1998), 376-386.
83. Y. Shi, R. Eberhart and Y. Chen, Implementation of evolutionary fuzzy systems, IEEE Transactions on Fuzzy Systems 7(2) (1999), 109-119.
84. R. Silipo and M. Berthold, Discriminative power of input features in a fuzzy model, in: Advances in Intelligent Data Analysis (IDA-99), (Vol. LNCS 1642), D. Hand, J. Kok and M. Berthold, eds, Springer-Verlag, Heidelberg, Germany, 1999, pp. 87-98.
85. S.F. Smith, A learning system based on genetic adaptive algorithms. Ph.D. Thesis. University of Pittsburgh, USA, 1980.
86. T. Sudkamp, J. Knapp and A. Knapp, Refine and merge: generating small bases from training data, in: Proceedings of the 9th IFSA World Congress and the 20th NAFIPS International Conference, Vancouver, Canada, 2001, pp. 197-202.
87. M. Sugeno and G.T. Kang, Structure identification of fuzzy model, Fuzzy Sets and Systems 28 (1988), 15-33.
88. M. Sugeno and T. Yasukawa, A fuzzy-logic-based approach to qualitative modeling, IEEE Transactions on Fuzzy Systems 1(1) (1993), 7-31.
89. T. Takagi and M. Sugeno, Fuzzy identification of systems and its application to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics 15 (1985), 116-132.
90. G. Venturini, SIA: A supervised inductive algorithm with genetic search for learning attribute based concepts, in: Proceedings of the European Conference on Machine Learning, Vienna, Austria, 1993, pp. 280-296.
91. L.-X. Wang and J.M. Mendel, Generating fuzzy rules by learning from examples, IEEE Transactions on Systems, Man, and Cybernetics 22(6) (1992), 1414-1427.
92. X. Wang and J. Hong, Learning optimization in simplifying fuzzy rules, Fuzzy Sets and Systems 106(3) (1999), 349-356.
93. Y. Yam, P. Baranyi and C.-T. Yang, Reduction of fuzzy rule base via singular value decomposition, IEEE Transactions on Fuzzy Systems 7(2) (1999), 120-132.
94. J. Yen and L. Wang, Simplifying fuzzy rule-based models using orthogonal transformation methods, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 29(1) (1999), 13-24.
95. J. Yen, L. Wang and C.W. Gillespie, Improving the interpretability of TSK fuzzy models by combining global learning and local learning, IEEE Transactions on Fuzzy Systems 6(4) (1998), 530-537.
96. W. Yu and Z. Bien, Design of fuzzy logic controller with inconsistent rule base, Journal of Intelligent and Fuzzy Systems 2(1994), 147-159.
97. L.A. Zadeh, Toward a theory of fuzzy systems, in: Aspects of network and system theory, Rinehart and Winston, New York, NY, USA, 1971, pp. 469-490.
98. L.A. Zadeh, Outline of a new approach to the analysis of complex systems and desision processes, IEEE Transactions on Systems, Man, and Cybernetics 3 (1973), 28-44.
99. L.A. Zadeh, On the analysis of large scale systems, in: Systems approaches and environment problems, Vandenhoeck and Ruprecht, Gottingen, Germany, 1974, pp. 23-37.
100. L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning, Parts I, II and III Information Science 8, 8, 9 (1975), 199-249, 301-357, 43-80.
101. L.A. Zadeh, A fuzzy-algorithmic approach to the definition of complex or imprecise concepts, International Journal on Man-Machine Studies 8(3) (1976), 249-291.
102. L.A. Zadeh, Soft computing and fuzzy logic, IEEE Software 11(6) (1994), 48-56.
103. L.A. Zadeh, Fuzzy logic and the calculi of fuzzy rules and fuzzy graphs: a precis, International Journal of Multiple-Valued Logic 1 (1996), 1-38.