By Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena
Fuzzy modeling often comes with contradictory standards: interpretability, that's the potential to specific the genuine procedure habit in a understandable manner, and accuracy, that is the aptitude to faithfully characterize the true procedure. during this framework, some of the most vital parts is linguistic fuzzy modeling, the place the legibility of the bought version is the most target. This activity is mostly constructed through linguistic (Mamdani) fuzzy rule-based platforms. An lively learn zone is orientated in the direction of using new options and constructions to increase the classical, inflexible linguistic fuzzy modeling with the most target of accelerating its precision measure. often, this accuracy development has been conducted with out contemplating the corresponding interpretability loss. at the moment, new tendencies were proposed attempting to guard the linguistic fuzzy version description strength through the optimization technique. Written through top specialists within the box, this quantity collects a few consultant researcher that pursue this procedure.
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Extra info for Accuracy Improvements in Linguistic Fuzzy Modeling
To appear. 13. J. Casillas, O. Cordon, and F. Herrera. Can linguistic modeling be as accurate as fuzzy modeling without losing its description to a high degree? Technical Report #DECSAI-00-01-20, Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain, 2000. es/~casillas/. 14. J. Casillas, O. Cordon, and F. Herrera. Learning fuzzy rules using ants colony algorithms. In Proceedings of the 2nd International Workshop on Ant Algorithms, pages 13-21, Brussels, Belgium, 2000.
Casillas, O. 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, pages 15701574, Vancouver, Canada, 2001. 12. J. Casillas, O. Cordon, and F. Herrera. COR: A methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics. To appear. 13.
Sendhoff. On generat ing FC 3 fuzzy rule systems from data using evolution strategies. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 29(4):829-845,1999. 46. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26-33, 1991. 47. F. Klawonn. Fuzzy sets and vague environments. Fuzzy Sets and Systems, 66:207-221, 1994. 48. K. KrishnaKumar and A. Satyadas. GA-optimized fuzzy controller for spacecraft attitude control. In J. Periaux, G. Winter, M. Gal1in, and P.
Accuracy Improvements in Linguistic Fuzzy Modeling by Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena