Accuracy Improvements in Linguistic Fuzzy Modeling - download pdf or read online

By Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena

ISBN-10: 3540370587

ISBN-13: 9783540370581

ISBN-10: 3642057039

ISBN-13: 9783642057038

Fuzzy modeling frequently comes with contradictory requisites: interpretability, that is the aptitude to precise the genuine approach habit in a understandable approach, and accuracy, that's the aptitude to faithfully signify the genuine procedure. during this framework, essentially the most very important parts is linguistic fuzzy modeling, the place the legibility of the bought version is the most goal. This job is mostly constructed by way of linguistic (Mamdani) fuzzy rule-based structures. An lively learn zone is orientated in the direction of using new innovations and constructions to increase the classical, inflexible linguistic fuzzy modeling with the most objective of accelerating its precision measure. often, this accuracy development has been performed with no contemplating the corresponding interpretability loss. at the moment, new traits were proposed attempting to guard the linguistic fuzzy version description strength throughout the optimization strategy. Written through prime specialists within the box, this quantity collects a few consultant researcher that pursue this process.

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Handling of inconsistent rules with an extended model of fuzzy reasoning. Joumal of Intelligent and Fuzzy Systems, 7:55-73, 1999. W. R. C. Gomide. Nonlinear context adaptation in the calibration of fuzzy sets. Fuzzy Sets and Systems, 88(1):91-97, 1997. M. Setnes and H. Hellendoorn. Orthogonal transforms for ordering and reduction of fuzzy rules. In Proceedings of the 9th IEEE International Conference on Fuzzy Systems, pages 700--705, San Antonio, TX, USA, 2000. Y. Shi, R. Eberhart, and Y. Chen.

A fuzzy-set theoretic interpretation of linguistic hedges. Journal of Cybernetics, 2(2):4-34, 1972. 87. A. Zadeh. Outline of a new approach to the analysis of complex systems and desision processes. IEEE Transactions on Systems, Man, and Cybernetics, 3:28-44, 1973. 88. A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning. Parts I, II and III. Information Science, 8, 8, 9:199-249, 301-357, 43-80, 1975. es Abstract. The chapter introduces a simple learning methodology, the cooperative rules (COR) one, that improves the accuracy of linguistic fuzzy models preserving the highest interpretability.

In B. BouchonMeunier, J. Gutierrez-Rios, L. R. Yager, editors, Techniques for constructing intelligent systems, volume 1, pages 321-334. Physica-Verlag, Heidelberg, Germany, 2002. 5. O. J. del Jesus, and F. Herrera. Genetic learning of fuzzy rule-based classification systems cooperating with fuzzy reasoning methods. International Joumal of Intelligent Systems, 13:1025-1053, 1998. 6. O. Cordon and F. Herrera. A proposal for improving the accuracy of linguistic modeling. IEEE Transactions on Fuzzy Systems, 8(3):335-344, 2000.

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Accuracy Improvements in Linguistic Fuzzy Modeling by Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena

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