Geração de Regras Fuzzy com Pré-Seleção de Regras Candidatas
Resumo
A definição da Base de Regras é uma das tarefas mais importantes e difíceis para o projeto de um Sistema Fuzzy. O uso de algoritmos genéticos para criar BRFs tem sido amplamente pesquisado. Este artigo descreve a pesquisa feita sobre a criação de Bases de Regras Fuzzy usando algoritmo genético em associação com uma heurística de pré-seleção de regras candidatas. Esta heurística está relacionada ao grau de cobertura de cada regra possível para o problema, o que permite a seleção de um conjunto de regras a ser usado pelo algoritmo genético, ao invés de trabalhar com todas as regras possíveis.Referências
Angelov, P. P. (2003). An evolutionary approach to fuzzy rule-based model synthesis using indices for rules. Fuzzy Sets and Systems, 137:325–338.
Berg, J., Kaymak, U., and Bergh, W. (2002). Fuzzy classification using probability-based rule weighting. In Proceedings of the 11th IEEE International Conference on Fuzzy Systems. Hawaii USA.
Casillas, J., Cordón, O., Jesus, M., and Herrera, F. (2005). Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Transactions on Fuzzy Systems, 13:13–29.
Castro, P. A. D. and Camargo, H. A. (2004). Learning and optimization of fuzzy rule base by means of self adaptative genetic algorithms. IEEE International Conference on Fuzzy Systems, 2:1037–1042. Budapeste Hungria.
Chi, Z., Yan, H., and Pham, T. (1996). Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition. World Scientific.
Cordón, O. and Herrera, F. (1997). A three stage evolutionary process for learning descriptive and approximate fuzzy logic controller knowledge bases from examples. International Journal of Approxiamate Reasoning, 7-4:369–407.
Cordón, O. and Herrera, F. (2001). Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems. Fuzzy Sets and Systems, 118:235–255.
Cordón, O., Herrera, F., and Villar, P. (2001). Generating the knowledge base of a fuzzy-based system by the genetic learning of the data base. IEEE Transactions on Fuzzy Systems, 9(4):667–674.
Cordon, O., Gomide, F. A. C., Herrera, F., Hoffmann, F., and Magdalena, L. (2004). Special issue on genetic fuzzy systems. Fuzzy Sets and Systems, 141.
Dumitrescu, D., Lazzerini, B., and Jair, L. (2000). Fuzzy Sets and Their Application to Clustering and Training. International Series on Computational Intelligence. CBC Press.
González, A. and Pérez, R. (1999). SLAVE: A genetic learning system based on an iterative approach. IEEE Transactions on Fuzzy Systems, 7:176–191.
Gurocak, H. B. (1999). A genetic-algorithm-based method for tuning fuzzy logic controllers. Fuzzy Sets and Systems, 108-1:39–47.
Hoffmann, F. (2004). Combining boosting and evolutionary algorithms for learning of fuzzy classification rules. Fuzzy Sets and Systems, 141:47–58.
Ichiba, D., Hara, K., and Kanoh, H. (2006). Spatial interpolation of traffic data by genetic fuzzy system. International Symposium on Evolving Fuzzy Systems, pages 280–285. Ambleside Inglaterra.
Ishibuchi, H., Murata, T., and Turksen, I. B. (1997). Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets and Systems, 89:134–150.
Ishibuchi, H. and Yamamoto, T. (2004). Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems, 141:59–88.
Jang, S. R., Sun, C. T., and Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing. Prentice Hall.
Kim, M., Kim, C., and Lee, J. (2006). Evolving compact and interpretable takagi-sugeno fuzzy models with a new encoding scheme. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 36:1006–1023.
Liao, T. W., Celmins, A. K., and Hammell, R. J. (1997). A fuzzy C-Means variant for the generation of fuzzy term sets. Fuzzy Sets and Fuzzy Systems, 135:241–257.
Merz, C. J. and Murphy, P. M. (1998). UCI repository of machine learning databases, [link]. Irvine, CA, UCLA.
Nomura, H., Hayashi, L., and Wakami, N. (1992). A learning method of fuzzy inference rules by descent method. In IEEE International Conference on Fuzzy Systems, pages 203–210. San Diego USA.
Pedrycz, W. and Gomide, F. (1998). An Introduction to Fuzzy Sets. MIT Press.
Rivas, V. M., Merelo, J. J., Rojas, I., Romero, G., Castillo, P. A., and Carpio, J. (2003). Evolving two-dimensional fuzzy systems. Fuzzy Sets and Systems, 138:381–398.
Spiegel, D. and Sudkamp, T. (2003). Sparse data in the evolutionary generation of fuzzy models. Fuzzy Sets and Systems, 138:363–379.
Wang, L. (2003). The WM method completed: a flexible fuzzy system approach to data mining. IEEE Trans. on Fuzzy Systems, 11:768–782.
Wang, L. and Mendel, J. (1992). Generating fuzzy rules by learning from examples. IEEE Trans. on SMC, 22:414–1427.
Berg, J., Kaymak, U., and Bergh, W. (2002). Fuzzy classification using probability-based rule weighting. In Proceedings of the 11th IEEE International Conference on Fuzzy Systems. Hawaii USA.
Casillas, J., Cordón, O., Jesus, M., and Herrera, F. (2005). Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Transactions on Fuzzy Systems, 13:13–29.
Castro, P. A. D. and Camargo, H. A. (2004). Learning and optimization of fuzzy rule base by means of self adaptative genetic algorithms. IEEE International Conference on Fuzzy Systems, 2:1037–1042. Budapeste Hungria.
Chi, Z., Yan, H., and Pham, T. (1996). Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition. World Scientific.
Cordón, O. and Herrera, F. (1997). A three stage evolutionary process for learning descriptive and approximate fuzzy logic controller knowledge bases from examples. International Journal of Approxiamate Reasoning, 7-4:369–407.
Cordón, O. and Herrera, F. (2001). Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems. Fuzzy Sets and Systems, 118:235–255.
Cordón, O., Herrera, F., and Villar, P. (2001). Generating the knowledge base of a fuzzy-based system by the genetic learning of the data base. IEEE Transactions on Fuzzy Systems, 9(4):667–674.
Cordon, O., Gomide, F. A. C., Herrera, F., Hoffmann, F., and Magdalena, L. (2004). Special issue on genetic fuzzy systems. Fuzzy Sets and Systems, 141.
Dumitrescu, D., Lazzerini, B., and Jair, L. (2000). Fuzzy Sets and Their Application to Clustering and Training. International Series on Computational Intelligence. CBC Press.
González, A. and Pérez, R. (1999). SLAVE: A genetic learning system based on an iterative approach. IEEE Transactions on Fuzzy Systems, 7:176–191.
Gurocak, H. B. (1999). A genetic-algorithm-based method for tuning fuzzy logic controllers. Fuzzy Sets and Systems, 108-1:39–47.
Hoffmann, F. (2004). Combining boosting and evolutionary algorithms for learning of fuzzy classification rules. Fuzzy Sets and Systems, 141:47–58.
Ichiba, D., Hara, K., and Kanoh, H. (2006). Spatial interpolation of traffic data by genetic fuzzy system. International Symposium on Evolving Fuzzy Systems, pages 280–285. Ambleside Inglaterra.
Ishibuchi, H., Murata, T., and Turksen, I. B. (1997). Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets and Systems, 89:134–150.
Ishibuchi, H. and Yamamoto, T. (2004). Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems, 141:59–88.
Jang, S. R., Sun, C. T., and Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing. Prentice Hall.
Kim, M., Kim, C., and Lee, J. (2006). Evolving compact and interpretable takagi-sugeno fuzzy models with a new encoding scheme. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 36:1006–1023.
Liao, T. W., Celmins, A. K., and Hammell, R. J. (1997). A fuzzy C-Means variant for the generation of fuzzy term sets. Fuzzy Sets and Fuzzy Systems, 135:241–257.
Merz, C. J. and Murphy, P. M. (1998). UCI repository of machine learning databases, [link]. Irvine, CA, UCLA.
Nomura, H., Hayashi, L., and Wakami, N. (1992). A learning method of fuzzy inference rules by descent method. In IEEE International Conference on Fuzzy Systems, pages 203–210. San Diego USA.
Pedrycz, W. and Gomide, F. (1998). An Introduction to Fuzzy Sets. MIT Press.
Rivas, V. M., Merelo, J. J., Rojas, I., Romero, G., Castillo, P. A., and Carpio, J. (2003). Evolving two-dimensional fuzzy systems. Fuzzy Sets and Systems, 138:381–398.
Spiegel, D. and Sudkamp, T. (2003). Sparse data in the evolutionary generation of fuzzy models. Fuzzy Sets and Systems, 138:363–379.
Wang, L. (2003). The WM method completed: a flexible fuzzy system approach to data mining. IEEE Trans. on Fuzzy Systems, 11:768–782.
Wang, L. and Mendel, J. (1992). Generating fuzzy rules by learning from examples. IEEE Trans. on SMC, 22:414–1427.
Publicado
30/06/2007
Como Citar
CINTRA, Marcos Evandro; CAMARGO, Heloisa de Arruda.
Geração de Regras Fuzzy com Pré-Seleção de Regras Candidatas. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 6. , 2007, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2007
.
p. 1341-1350.
ISSN 2763-9061.
