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Nonlinear Lagrangean Neural Networks

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Artificial Neural Networks and Machine Learning – ICANN 2021 (ICANN 2021)

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Abstract

Recently, Fagerholm, Friston, Moran and Leech advanced a class of linear neural network models complying with Lagrangean dynamics. In the present effort, we explore the possibility of extending the Lagrangean approach to nonlinear models. We present a Lagrangean formalism for a family of nonlinear neural network models, and investigate its main mathematical features.

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Author information

Authors and Affiliations

  1. Instituto de Matemática e Estatística, Universidade do Estado do Rio de Janeiro, Rua São Francisco Xavier 524, Rio de Janeiro, RJ, 20550-900, Brazil

    Roseli S. Wedemann

  2. CeBio y Departamento de Ciencias Básicas, Universidad Nacional del Noroeste de la Provincia de Buenos Aires, UNNOBA, Conicet, Roque Saenz Peña 456, Junin, Argentina

    Angel Ricardo Plastino

Authors
  1. Roseli S. Wedemann
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  2. Angel Ricardo Plastino
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Corresponding author

Correspondence to Roseli S. Wedemann .

Editor information

Editors and Affiliations

  1. Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. iMotions A/S, Copenhagen, Denmark

    Paolo Masulli

  3. University of Tübingen, Tübingen, Baden-Württemberg, Germany

    Sebastian Otte

  4. Universität Hamburg, Hamburg, Germany

    Stefan Wermter

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Wedemann, R.S., Plastino, A.R. (2021). Nonlinear Lagrangean Neural Networks. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12894. Springer, Cham. https://doi.org/10.1007/978-3-030-86380-7_14

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  • DOI: https://doi.org/10.1007/978-3-030-86380-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86379-1

  • Online ISBN: 978-3-030-86380-7

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