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Intuitionistic Fuzzy Deep Neural Network

Author

Listed:
  • Krassimir Atanassov

    (Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. Georgi Bonchev Str., Bl. 105, 1113 Sofia, Bulgaria
    Intelligent Systems Laboratory, Prof. Dr. Assen Zlatarov University, 1 “Prof. Yakimov” Blvd., 8010 Burgas, Bulgaria)

  • Sotir Sotirov

    (Intelligent Systems Laboratory, Prof. Dr. Assen Zlatarov University, 1 “Prof. Yakimov” Blvd., 8010 Burgas, Bulgaria)

  • Tania Pencheva

    (Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. Georgi Bonchev Str., Bl. 105, 1113 Sofia, Bulgaria)

Abstract

The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets, aiming to benefit from the advantages of both methods. The investigation presents in a methodological way the whole process of IFDNN development, starting with the simplest form—an intuitionistic fuzzy neural network (IFNN) with one layer with single-input neuron, passing through IFNN with one layer with one multi-input neuron, further subsequent complication—an IFNN with one layer with many multi-input neurons, and finally—the true IFDNN with many layers with many multi-input neurons. The formulas for strongly optimistic , optimistic , average , pessimistic and strongly pessimistic formulas for NN parameters estimation, represented in the form of intuitionistic fuzzy pairs, are given here for the first time for each one of the presented IFNNs. To demonstrate its workability, an example of an IFDNN application to biomedical data is here presented.

Suggested Citation

  • Krassimir Atanassov & Sotir Sotirov & Tania Pencheva, 2023. "Intuitionistic Fuzzy Deep Neural Network," Mathematics, MDPI, vol. 11(3), pages 1-14, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:716-:d:1052458
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