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Łukasiewicz Logic and Artificial Neural Networks

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Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and Their Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 835))

Abstract

In this paper we analyze connections between artificial neural networks, in particular multilayer perceptrons, and Łukasiewicz fuzzy logic. Theoretical results lead us to: connect Polynomial completeness and the study of input selection; use normal form of Łukasiewicz formulas to describe the structure of these multilayer perceptrons.

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Authors and Affiliations

  1. Department of Mathematics, University of Salerno, 84084, Fisciano, SA, Italy

    Antonio Di Nola & Gaetano Vitale

Authors
  1. Antonio Di Nola
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  2. Gaetano Vitale
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Corresponding author

Correspondence to Gaetano Vitale .

Editor information

Editors and Affiliations

  1. Department of Teacher Education, University of Texas at El Paso, El Paso, TX, USA

    Olga Kosheleva

  2. Institute of Computational Technologies SB RAS, Novosibirsk, Russia

    Sergey P. Shary

  3. Johns Creek, GA, USA

    Gang Xiang

  4. Deptartment of Informatics, The State Russian Museum, Saint Petersburg, Russia

    Roman Zapatrin

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Nola, A.D., Vitale, G. (2020). Łukasiewicz Logic and Artificial Neural Networks. In: Kosheleva, O., Shary, S., Xiang, G., Zapatrin, R. (eds) Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and Their Applications. Studies in Computational Intelligence, vol 835. Springer, Cham. https://doi.org/10.1007/978-3-030-31041-7_8

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