Download e-book for kindle: Accuracy Improvements in Linguistic Fuzzy Modeling by Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis

Combinatorics

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 specifications: interpretability, that is the potential to specific the true method habit in a understandable approach, and accuracy, that's the aptitude to faithfully signify the true procedure. during this framework, the most vital parts is linguistic fuzzy modeling, the place the legibility of the received version is the most goal. This job is generally built through linguistic (Mamdani) fuzzy rule-based structures. An lively learn region is orientated in the direction of using new options and buildings to increase the classical, inflexible linguistic fuzzy modeling with the most objective of accelerating its precision measure. regularly, this accuracy development has been conducted with out contemplating the corresponding interpretability loss. at present, new developments were proposed attempting to safeguard the linguistic fuzzy version description strength throughout the optimization procedure. Written via top specialists within the box, this quantity collects a few consultant researcher that pursue this strategy.

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Valente de Oliveira. Towards neuro-linguistic modeling: constraints for optimization of membership functions. Fuzzy Sets and Systems, 106(3):357-380, 1999. 81. -H. -P. -S. Tseng. Integrating fuzzy knowledge by genetic algorithms. IEEE Transactions on Evolutionary Computation, 2(4):138149, 1998. 82. -X. M. Mendel. Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22(6):1414-1427, 1992. 83. N. Xiong and L. Litz. Fuzzy modeling based on premise optimization.

1, whilst an example of the operat ion mode for a simple problem with two input variables and three labels for each linguistic variable is graphically illustrated in Fig. 2. Since the search space tackled in step 2. is usually large, it is necessary to use approximate search techniques. In [3], accurate linguistic models have been obtained using simulated annealing. Other techniques such as tabu search, genetic algorithms, and ant colony optimization (ACO) have been also applied to COR methodology [4].

J. Casillas, O. Cordon, and F. Herrera. Different approaches to induce cooperation in fuzzy linguistic models under the COR methodology. 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.

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


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