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Abstract:

Although Mamdani-type fuzzy rule-based systems (FRBSs) became successfully performing clearly interpretable fuzzy models, they still have some lacks related to their accuracy when solving complex problems. A variant of these kinds of systems, which allows to perform a more accurate model representation, are the so-called approximate FRBSs. This alternative representation still cannot avoid the problems concerning the fuzzy rule learning methods, which as prototype identification algorithms, try to extract those approximate rules from the object problem space. In this paper we deal with the previous problems, viewing fuzzy models as a class of local modeling approaches which attempt to solve a complex problem by decomposing it into a number of simpler subproblems with smooth transitions between them. In order to develop this class of models, we first propose a common framework to characterize available approximate fuzzy rule learning methods, and later we modify it by introducing a fuzzy rule base hierarchical learning methodology (FRB-HLM). This methodology is based on the extension of the simple building process of the fuzzy rule base of FRBSs in a hierarchical way, in order to make the system more accurate. This flexibilization will allow us to have fuzzy rules with different degrees of specificity, and thus to improve the modeling of those problem subspaces where the former models have bad performance, as a refinement. This approach allows us not to have to assume a fixed number of rules and to integrate the good local behavior of the hierarchical model with the global model, ensuring a good global performance. © 2001 Elsevier Science Inc.

Registro:

Documento: Artículo
Título:Fuzzy modeling by hierarchically built fuzzy rule bases
Autor:Cordón, O.; Herrera, F.; Zwir, I.
Filiación:Department of Computer Science and Artificial Intelligence, ETS De Ingeniera Informatica, University of Granada, Avda. Andalucia 38, 18071 Granada, Spain
Department of Computer Science, University of Buenos Aires, 1428 Buenos Aires, Argentina
Palabras clave:Approximate fuzzy rules; Fuzzy modeling; Fuzzy rule base; Genetic algorithms; Hierarchical fuzzy clustering; Mamdani-type fuzzy rule-based systems; Algorithms; Approximation theory; Data structures; Hierarchical systems; Knowledge based systems; Learning systems; Mathematical models; Problem solving; Mamdani-type fuzzy rule-based systems; Fuzzy sets
Año:2001
Volumen:27
Número:1
Página de inicio:61
Página de fin:93
DOI: http://dx.doi.org/10.1016/S0888-613X(01)00034-2
Título revista:International Journal of Approximate Reasoning
Título revista abreviado:Int J Approximate Reasoning
ISSN:0888613X
CODEN:IJARE
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0888613X_v27_n1_p61_Cordon

Referencias:

  • Alcalá, R., Casillas, J., Cordón, O., Herrera, F., (1999) Approximate Mamdani-type fuzzy rule-based systems: features and taxonomy of learning methods, , Technical Report #DECSAI-990117, Department of Computer Science and Artificial Intelligence, E.T.S. de Ingeniería Informática, University of Granada, Spain
  • Babuška, R., (1998) Fuzzy Modeling for Control, , Kluwer Academic Publishers, Dordrecht
  • Bardossy, A., Duckstein, L., (1995) Fuzzy Rule-Based Modeling with Application to Geophysical, Biological and Engineering Systems, , CRC Press, Boca Raton
  • Bastian, A., How to handle the flexibility of linguistic variables with applications (1994) International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2 (4), pp. 463-484
  • Bezdek, J.C., (1973) Fuzzy Mathematics in Pattern Classification, , Ph.D. thesis, Cornell University
  • Bezdek, J.C., Pal, S., (1992) Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data, , IEEE Press, New York
  • Bezdek, J.C., Fuzzy clustering (1998) Handbook of Fuzzy Computation, , E.H. Ruspini, P.P. Bonisone, W. Pedrycz (Eds.), Institute of Physics Press
  • Carse, B., Fogarty, T.C., Munro, A., Evolving fuzzy rule based controllers using genetic algorithms (1996) Fuzzy Sets and Systems, 80, pp. 273-294
  • Cordón, O., Herrera, F., Hybridizing genetic algorithms and evolutionary strategies to design approximate fuzzy rule-based systems (2001) Fuzzy Sets and Systems, 118 (2), pp. 235-255
  • Cordón, O., Herrera, F., Peregrín, A., Applicability of the fuzzy operators in the design of fuzzy logic controllers (1997) Fuzzy Sets and Systems, 86, pp. 15-41
  • Cordón, O., Herrera, F., A three-stage evolutionary process for learning descriptive and approximative fuzzy logic controller knowledge bases from examples (1997) International Journal of Approximate Reasoning, 17 (4), pp. 369-407
  • Cordón, O., Herrera, F., Sánchez, L., Solving electrical distribution problems using hybrid evolutionary data analysis techniques (1999) Applied Intelligence, 10, pp. 5-24
  • Cordón, O., Herrera, F., Zwir, I., (1999) Linguistic modeling by hierarchical systems of linguistic rules, , Technical Report #DECSAI-99114, Department of Computer Science and Artificial Intelligence, E.T.S. de Ingeniería Informática, University of Granada, Spain
  • Cordón, O., Herrera, F., Villar, P., Analysis and guidelines to obtain a good uniform fuzzy partition granularity for FRBSs using simulated annealing (2000) International Journal of Approximate Reasoning, 25 (3), pp. 187-215
  • Cordón, O., Herrera, F., Zwir, I., (2000) A hierarchical knowledge-based environment for linguistic modeling: models and methodology, , #DECSAI-000106, Department of Computer Science and Artificial Intelligence, University of Granada, Spain
  • Cordón, O., Herrera, F., Zwir, I., Hierarchical knowledge bases for fuzzy rule-based systems (2000) Proceedings of the 8th Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU), pp. 1770-1777. , Madrid, Spain
  • Delgado, M., Gómez-Skarmeta, A.F., Vila, A., On the use of hierarchical clustering in fuzzy modeling (1996) International Journal of Approximate Reasoning, 14, pp. 237-257
  • Delgado, M., Gómez-Skarmeta, A.F., Marín, F., A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling (1997) IEEE Transactions on Fuzzy Systems, 5 (2), pp. 223-232
  • Hand, D.J., (1992) Discrimination and Classification, , Wiley, New York
  • Herrera, F., Lozano, M., Verdegay, J.L., A learning process for fuzzy control rules using genetic algorithms (1998) Fuzzy Sets and Systems, 100, pp. 143-158
  • Hirota, K., (1993) Industrial Applications of Fuzzy Technology, , Springer, Berlin
  • Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, H., Selecting fuzzy if-then rules for classification problems using genetic algorithms (1995) IEEE Transactions on Fuzzy Systems, 3 (3), pp. 260-270
  • Krishnapuram, R., Keller, J., A possibilistic approach to clustering (1993) IEEE Transactions on Fuzzy Systems, pp. 98-110
  • Leondes, C.T., (2000) Fuzzy Theory Systems, Techniques and Applications, , Academic Press, New York
  • Mitchell, T., (1997) Machine Learning, , McGraw-Hill, New York
  • Pedrycz, W., (1996) Fuzzy Modelling: Paradigms and Practice, , Kluwer Academic Press, Dordrecht
  • Pedrycz, W., Vasilakos, A.V., Linguistic models and linguistic modeling (1999) IEEE Transactions on Systems, Man, and Cybernetics, 29 (6)
  • Ruspini, E.H., A new approach to clustering (1969) Information and Control, 15 (1), pp. 22-32
  • Ruspini, E.H., Zwir, I.S., Automated qualitative description of measurements (1999) Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference, , Venice, Italy
  • Sánchez, L., (1997) Study of the Asturias rural and urban low voltage network, , Technical Report, Hidroeléctrica del Cantábrico Research and Development Department (in spanish), Asturias, Spain
  • Sánchez, L., Interval-valued GA-P algorithms (2000) IEEE Transactions on Evolutionary Computation, 4 (1), pp. 64-72
  • Sugeno, M., Yasukawa, T., A fuzzy-logic-based approach to qualitative modeling (1993) IEEE Transactions on Fuzzy Systems, 1 (1), pp. 7-31
  • Wang, L.X., Mendel, J.M., Generating fuzzy rules by learning from examples (1992) IEEE Transactions on Systems, Man, and Cybernetics, 22, pp. 1414-1427
  • Yen, J., Wang, L., Wayne Gillespie, C., Improving the interpretability of TSK fuzzy models by combining global learning and local learning (1998) IEEE Transactions on Fuzzy Systems, 6 (4), pp. 530-537
  • Zadeh, L.A., Fuzzy sets (1965) Information and Control, 8, pp. 338-353
  • Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning (1975) Information Science Part I, 8, pp. 199-249
  • Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning (1975) Information Science (Part II), 8, pp. 301-357
  • Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning (1975) Information Science (Part III), 9, pp. 43-80
  • Zadeh, L.A., Toward a theory of information granulation and its centrality in human reasoning and fuzzy logic (1997) Fuzzy Sets and Systems, 90, pp. 111-127
  • Zwir, I., Ruspini, E.H., Qualitative object description: Initial reports of the exploration of the frontier (1999) Proceedings of the EUROFUSE-SIC '99, pp. 485-490. , Budapest, Hungary

Citas:

---------- APA ----------
Cordón, O., Herrera, F. & Zwir, I. (2001) . Fuzzy modeling by hierarchically built fuzzy rule bases. International Journal of Approximate Reasoning, 27(1), 61-93.
http://dx.doi.org/10.1016/S0888-613X(01)00034-2
---------- CHICAGO ----------
Cordón, O., Herrera, F., Zwir, I. "Fuzzy modeling by hierarchically built fuzzy rule bases" . International Journal of Approximate Reasoning 27, no. 1 (2001) : 61-93.
http://dx.doi.org/10.1016/S0888-613X(01)00034-2
---------- MLA ----------
Cordón, O., Herrera, F., Zwir, I. "Fuzzy modeling by hierarchically built fuzzy rule bases" . International Journal of Approximate Reasoning, vol. 27, no. 1, 2001, pp. 61-93.
http://dx.doi.org/10.1016/S0888-613X(01)00034-2
---------- VANCOUVER ----------
Cordón, O., Herrera, F., Zwir, I. Fuzzy modeling by hierarchically built fuzzy rule bases. Int J Approximate Reasoning. 2001;27(1):61-93.
http://dx.doi.org/10.1016/S0888-613X(01)00034-2