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: Automatic Learning Techniques in Power Systems
  name:
    name part: Wehenkel, Louis A.
    role:
      role term: creator
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: mau
    date issued: 1998
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (xxix, 280 pages)
  abstract: Automatic learning is a complex, multidisciplinary field of research and development, involving theoretical and applied methods from statistics, computer science, artificial intelligence, biology and psychology. Its applications to engineering problems, such as those encountered in electrical power systems, are therefore challenging, while extremely promising. More and more data have become available, collected from the field by systematic archiving, or generated through computer-based simulation. To handle this explosion of data, automatic learning can be used to provide systematic approaches, without which the increasing data amounts and computer power would be of little use. Automatic Learning Techniques in Power Systems is dedicated to the practical application of automatic learning to power systems. Power systems to which automatic learning can be applied are screened and the complementary aspects of automatic learning, with respect to analytical methods and numerical simulation, are investigated. This book presents a representative subset of automatic learning methods - basic and more sophisticated ones - available from statistics (both classical and modern), and from artificial intelligence (both hard and soft computing). The text also discusses appropriate methodologies for combining these methods to make the best use of available data in the context of real-life problems. Automatic Learning Techniques in Power Systems is a useful reference source for professionals and researchers developing automatic learning systems in the electrical power field.
  note: by Louis A. Wehenkel.
  note: Electronic resource.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Text processing (Computer science)
  subject:
    topic: Computer engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer engineering
  subject:
    topic: Engineering
  subject:
    topic: Text processing (Computer science)
  classification: TK1-9971
  classification: 621.3
  related item:
    title info:
      title: Springer International Series in Engineering and Computer Science, Power Electronics and Power Systems
  identifier: 9781461554516 (electronic bk.)
  identifier: 1461554519 (electronic bk.)
  identifier: 9781461374893
  identifier: 1461374898
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: AU@
    record creation date: 121227
    record change date: 20260420215938.8
    record identifier: in00003581629
    language of cataloging:
      language term: eng