PublisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
Rights accessOpen Access
This 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.
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