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Nuclear Phenomenology with Neural Nets

  • Conference paper
Neural Network Dynamics

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

We propose a new method of phenomenological analysis of physical systems based on adaptive neural networks. When trained with the backpropagation algorithm, multilayered networks are capable of learning the associations between dependent and independent variables implicit in large data sets and may show reliable predictive power when tested on examples absent from the training set. The approach is illustrated through applications to several problems relating to the stability of atomic nuclei.

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Author information

Authors and Affiliations

  1. McDonnell Center for the Space Sciences and Department of Physics, Washington University, St. Louis, MO, 63130, USA

    John W. Clark & Srinivas Gazula

  2. School of Chemical Sciences, University of Illinois, Urbana, IL, 61801, USA

    Henrik Bohr

Authors
  1. John W. Clark
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  2. Srinivas Gazula
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  3. Henrik Bohr
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Editor information

Editors and Affiliations

  1. Centre for Neural Networks, Department of Mathematics, King’s College, Strand, London, WC2R 2LS, UK

    J. G. Taylor BA, BSc, MA, PhD, FlnstP (Director) (Director)

  2. Dipartimento di Fisica Teorica, Università di Salerno, Via S. Allende, 84081, Baronissi (SA), Italy

    E. R. Caianiello

  3. Department of Biophysics, The Technical University of Denmark, Building 307, DK-2800, Lyngby, Denmark

    R. M. J. Cotterill BSc, MS, PhD, DSc, FlnstP, C.Phys.

  4. Department of Physics, Washington University, St Louis, Missouri, 63130, USA

    J. W. Clark

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© 1992 Springer-Verlag London Limited

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Clark, J.W., Gazula, S., Bohr, H. (1992). Nuclear Phenomenology with Neural Nets. In: Taylor, J.G., Caianiello, E.R., Cotterill, R.M.J., Clark, J.W. (eds) Neural Network Dynamics. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2001-8_21

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  • DOI: https://doi.org/10.1007/978-1-4471-2001-8_21

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19771-3

  • Online ISBN: 978-1-4471-2001-8

  • eBook Packages: Springer Book Archive

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