rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Scientific Applications of Neural Nets
    sub title: Proceedings of the 194th W.E. Heraeus Seminar Held at Bad Honnef, Germany, 11-13 May 1998
  name:
    name part: Clark, John W.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Lindenau, Thomas.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Ristig, Manfred L.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 1999
    edition: 1st ed. 1999.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 290 p. 78 illus., 6 illus. in color. online resource.
  abstract: Neural-network models for event analysis are widely used in experimental high-energy physics, star/galaxy discrimination, control of adaptive optical systems, prediction of nuclear properties, fast interpolation of potential energy surfaces in chemistry, classification of mass spectra of organic compounds, protein-structure prediction, analysis of DNA sequences, and design of pharmaceuticals. This book, devoted to this highly interdisciplinary research area, addresses scientists and graduate students. The pedagogically written review articles range over a variety of fields including astronomy, nuclear physics, experimental particle physics, bioinformatics, linguistics, and information processing.
  table of contents: Neural networks: New tools for modelling and data analysis in science -- Adaptive optics: Neural network wavefront sensing, reconstruction, and prediction -- Nuclear physics with neural networks -- Using neural networks to learn energy corrections in hadronic calorimeters -- Neural networks for protein structure prediction -- Evolution teaches neural networks to predict protein structure -- An application of artificial neural networks in linguistics -- Optimization with neural networks -- Dynamics of networks and applications.
  note: edited by John W. Clark, Thomas Lindenau, Manfred L. Ristig.
  subject:
    topic: Statistical physics
  subject:
    topic: Dynamical systems
  subject:
    topic: Nuclear physics
  subject:
    topic: Artificial intelligence
  subject:
    topic: Complex Systems
  subject:
    topic: Particle and Nuclear Physics
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Statistical Physics and Dynamical Systems
  classification: QC174.7-175.36
  classification: 621
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Physics, 522
  identifier: 9783540489801
  identifier: https://doi.org/10.1007/BFb0104276
  record info:
    record creation date: 121227
    record change date: 20191027031847.0
    record identifier: 978-3-540-48980-1