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• W2103221808 abstract "The achievement of approximation models may constitute a complex computational task, in the cases of models with non-linear relation between parameters and data. This problem becomes even harder when the system to be modeled is subject to noisy data, since the simple minimization of error over a training data set can give rise to misleading models that fit both the system structure and the noise (the phenomenon of model <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>overfit</i> ). This paper proposes a multiobjective genetic algorithm for guiding the training of ANFIS fuzzy networks. This algorithm considers the complexity of network jointly with the error over the training set as relevant objectives, that should be minimized. Results obtained in three regression problems are presented to show the generalization capacity of models constructed with the proposed methodology." @default.
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• W2103221808 date "2008-06-01" @default.
• W2103221808 modified "2023-09-24" @default.
• W2103221808 title "A genetic algorithm for multiobjective training of ANFIS fuzzy networks" @default.
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• W2103221808 doi "https://doi.org/10.1109/cec.2008.4631239" @default.
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