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dc.contributor.authorSaade, J.J.
dc.description.abstractThis paper presents a new learning algorithm for the design of Mamdani-type or fully-linguistic fuzzy controllers based on available input-output data. It relies on the use of a previously introduced parametrized defuzzification strategy. The learning scheme is supported by an investigated property of the defuzzification method. In addition, the algorithm is tested by considering a typical non-linear function that has been adopted in a number of published research articles. The test stresses on data-fitting, function shape representation, noise insensitivity and generalization capability. The results are compared with those obtained using neuro-fuzzy and other fuzzy system design approaches.
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 2000 Vol. 7 Núm. 2 [ -3 ]
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.subject.otherLearning algorithm
dc.subject.otherMamdani-type fuzzy controllers
dc.titleA defuzzification based new algorithm for the design of Mamdani-type fuzzy controllers
dc.subject.lemacSistemes de control
dc.subject.lemacAprenentatge automàtic -- Algorismes
dc.subject.lemacControladors programables
dc.subject.amsClassificació AMS::93 Systems Theory; Control::93C Control systems, guided systems
dc.rights.accessOpen Access

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