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

  • Paulo Vitor de Campos SouzaEmail author
  • Luiz Carlos Bambirra Torres
  • Augusto Junio Guimaraes
  • Vanessa Souza Araujo
  • Vincius Jonathan Silva Araujo
  • Thiago Silva Rezende
Methodologies and Application
  • 15 Downloads

Abstract

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.

Keywords

DDC Fuzzy neural networks Pattern classification RAF 

Notes

Acknowledgements

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

Compliance with ethical standards

Conflict of interest

All authors declare no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Paulo Vitor de Campos Souza
    • 1
    • 2
    Email author
  • Luiz Carlos Bambirra Torres
    • 2
  • Augusto Junio Guimaraes
    • 1
  • Vanessa Souza Araujo
    • 1
  • Vincius Jonathan Silva Araujo
    • 1
  • Thiago Silva Rezende
    • 1
  1. 1.Faculty UNA of BetimBetimBrazil
  2. 2.Federal University of Minas GeraisBelo HorizonteBrazil

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