Function approximation capability of a novel fuzzy flip-flop based Neural Network

Rita Lovassy, László T. Kóczy, László Gál

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

The function approximation capability of various connectionist systems has been one of the most interesting problems. A method for constructing Multilayer Perceptron Neural Networks (MLP NN) with the aid of fuzzy operations based flip-flops able to approximate single and multiple variable functions is proposed. This paper introduces the concept of fuzzy flip-flop based neural network, particularly by deploying three types of fuzzy flip-flops as neurons. A comparative study of feedbacked fuzzy J-K and two kinds of fuzzy D flip-flops used as neurons, based on fuzzy algebraic, Yager, Dombi, Hamacher and Frank operations is given. Simulation results are presented for several test functions.

Original languageEnglish
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages1900-1907
Number of pages8
DOIs
Publication statusPublished - Nov 18 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
CityAtlanta, GA
Period6/14/096/19/09

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Lovassy, R., Kóczy, L. T., & Gál, L. (2009). Function approximation capability of a novel fuzzy flip-flop based Neural Network. In 2009 International Joint Conference on Neural Networks, IJCNN 2009 (pp. 1900-1907). [5178849] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2009.5178849