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Applicability of the fuzzy operators in the design of fuzzy logic controllers.

, , and . Fuzzy Sets and Systems, 86 (1): 15-41 (1997)

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Efficient Distributed Genetic Algorithm for Rule Extraction., and . HIS, page 531-536. IEEE Computer Society, (2008)An efficient adaptive fuzzy inference system for complex and high dimensional regression problems in linguistic fuzzy modelling., , , and . Knowl.-Based Syst., (2013)Rule Base and Inference System Cooperative Learning of Mamdani Fuzzy Systems with Multiobjective Genetic Algorithms., , and . IFSA/EUSFLAT Conf., page 1045-1050. (2009)A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm., , and . Int. J. Comput. Intell. Syst., 5 (2): 297-321 (2012)A view of High Dimensional, Large-Scale and Big Data Fuzzy Rule based Regression and Control Systems., , , and . FUZZ-IEEE, page 1-6. IEEE, (2018)A Review of Distributed Data Models for Learning., , , and . HAIS, volume 10334 of Lecture Notes in Computer Science, page 88-97. Springer, (2017)Cooperation between the Inference System and the Rule Base by Using Multiobjective Genetic Algorithms., , and . HAIS, volume 5271 of Lecture Notes in Computer Science, page 739-746. Springer, (2008)Characterisation of implication operators in fuzzy rule based systems from basic properties., , and . EUSFLAT-ESTYLF Joint Conf., page 163-166. Universitat de les Illes Balears, Palma de Mallorca, Spain, (1999)Searching for basic properties obtaining robust implication operators in fuzzy control., , and . Fuzzy Sets and Systems, 111 (2): 237-251 (2000)Applicability of the fuzzy operators in the design of fuzzy logic controllers., , and . Fuzzy Sets and Systems, 86 (1): 15-41 (1997)