Data density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function


This paper proposes the use of fuzzification functions based on clustering of data based on their density to perform the granularization of the input space. The neurons formed in this layer are built through the density centers obtained with the input data of the model. In the second layer, the nullneurons aggregate the generated neurons in the first layer and allow the creation of if/then fuzzy rules. Even in the second layer, a regularization function is activated to determine the essential nullneurons. The concepts of extreme learning machine generate the weights used in the third layer, but with a regularizing factor. Finally, in the third layer, represented by an artificial neural network, it has a single neuron that the activation function uses robust functions to carry out the model. To verify the new training approach for fuzzy neural networks, we performed real and synthetic database tests for the pattern classification, which led to the conclusion that the data density-based approach the use of regularization factors in the second model layer and neurons with more robust activation functions allowed better results compared to other classifiers that use the concepts of extreme learning machine.

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    For the interval of bt = [8, 16, 32, 64] and \(\lambda \) = [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1].


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The thanks of this work are destined to CEFET-MG, University Center UNA.

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Correspondence to Paulo Vitor de Campos Souza.

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de Campos Souza, P.V., Torres, L.C.B., Guimaraes, A.J. et al. Data density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function. Soft Comput 23, 12475–12489 (2019).

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  • DDC
  • Fuzzy neural networks
  • Pattern classification
  • RAF