Fuzzy Neural Networks based on Fuzzy Logic Neurons Regularized by Resampling Techniques and Regularization Theory for Regression Problems

Authors

  • Paulo Vitor de Campos Souza Centro Federal de Educação Tecnológica de Minas Gerais
  • Augusto Junio Guimaraes
  • Vanessa Souza Araújo
  • Thiago Silva Rezende
  • Vinicius Jonathan Silva Araújo

DOI:

https://doi.org/10.4114/intartif.vol22iss63pp114-133

Keywords:

Bootstrap lasso, Extreme Learning Machines, Regression Problems, Fuzzy Neural Network, Fuzzy Logic Neurons

Abstract

This paper presents a novel learning algorithm for fuzzy logic neuron based on neural networks and fuzzy systems able to generate accurate and transparent models. The learning algorithm is based on ideas from Extreme Learning Machine [36], to achieve a low time complexity, and regularization theory, resulting in sparse and accurate models. A compact set of incomplete fuzzy rules can be extracted from the resulting network topology. Experiments considering regression problems are detailed. Results suggest the proposed approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability.

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Published

2018-11-09

How to Cite

Campos Souza, P. V. de, Junio Guimaraes, A., Souza Araújo, V., Silva Rezende, T., & Silva Araújo, V. J. (2018). Fuzzy Neural Networks based on Fuzzy Logic Neurons Regularized by Resampling Techniques and Regularization Theory for Regression Problems. Inteligencia Artificial, 21(62), 114-133. https://doi.org/10.4114/intartif.vol22iss63pp114-133

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