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: Neural network learning and expert systems
  name:
    name part: Gallant, Stephen I.
    role:
      role term: creator
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: mau
    place:
      place term: Cambridge, Mass
    publisher: MIT Press
    date issued: ©1993
    date issued: 1993
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xvi, 365 pages) : illustrations
  abstract: "Most neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this is the only currently available software permitting combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, color graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus. The book provides an introduction to neural networks and simulation, a tutorial on the software, and many complete programs including several backpropagation schemes, creeping random search, competitive learning with and without adaptive-resonance function and "conscience," counterpropagation, nonlinear Grossberg-type neurons, Hopfield-type and bidirectional associative memories, predictors, function learning, biological clocks, system identification, and more. In addition, the book introduces a simple, integrated environment for programming, displays, and report preparation. Even differential equations are entered in ordinary mathematical notation. Users need not learn C or LISP to program nonlinear neuron models. To permit truly interactive experiments, the extra-fast compilation is unnoticeable, and simulations execute faster than PC FORTRAN. The nearly 90 illustrations include block diagrams, computer programs, and simulation-output graphs."
  note: Stephen I. Gallant.
  note: "Granino A. Kom has been a Professor of Electrical Engineering at the University of Arizona and has worked in the aerospace industry for a decade. He is the author of ten other engineering texts and handbooks."
  note: "A Bradford Book."
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Expert systems (Computer science)
  subject:
    topic: Expert systems.
  subject:
    topic: COMPUTER SCIENCE/Machine Learning & Neural Networks
  classification: QA76.87 .G35 1993eb
  classification: 006.3
  identifier: 0585040281
  identifier: 9780585040288
  identifier: 0262071452
  identifier: 9780262071451
  identifier: 9780262273404
  identifier: 0262273403
  identifier: 9780262527897
  identifier: 0262527898
  identifier: 9780262273400
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: OCoLC-P
    record creation date: 000112
    record change date: 20221121162341.8
    record identifier: in00004600896
    language of cataloging:
      language term: eng