Robustness of fuzzy flip-flop based neural networks

Rita Lovassy, László T. Kóczy, László Gál

Research output: Conference contribution

1 Citation (Scopus)

Abstract

In this paper the robustness of three different types of Fuzzy Flip-Flop based Neural Network (FNN) and the standard tansig based neural networks is compared from the various test function approximation goodness points of view. It is tested how well the fuzzy flip-flop based and the simulated neural networks handle the test data sets outlier points. The robust design of the FNN is presented, and the best suitable fuzzy neuron type is emphasized. Furthermore, the sensitivity of fuzzy neural networks to the fuzzy neuron type and hidden layers neuron number is evaluated.

Original languageEnglish
Title of host publication11th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2010 - Proceedings
Pages207-211
Number of pages5
DOIs
Publication statusPublished - dec. 1 2010
Event11th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2010 - Budapest, Hungary
Duration: nov. 18 2010nov. 20 2010

Publication series

Name11th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2010 - Proceedings

Other

Other11th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2010
CountryHungary
CityBudapest
Period11/18/1011/20/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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  • Cite this

    Lovassy, R., Kóczy, L. T., & Gál, L. (2010). Robustness of fuzzy flip-flop based neural networks. In 11th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2010 - Proceedings (pp. 207-211). [5672248] (11th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2010 - Proceedings). https://doi.org/10.1109/CINTI.2010.5672248