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 Networks
    sub title: Methodology and Applications
  name:
    name part: Dreyfus, G.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2005
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 498 p. online resource.
  abstract: Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented introduction.
  table of contents: Neural Networks: An Overview -- Modeling with Neural Networks: Principles and Model Design Methodology -- Modeling Metholodgy: Dimension Reduction and Resampling Methods -- Neural Identification of Controlled Dynamical Systems and Recurrent Networks -- Closed-Loop Control Learning -- Discrimination -- Self-Organizing Maps and Unsupervised Classification -- Neural Networks without Training for Optimization.
  note: by G. Dreyfus.
  subject:
    topic: Physics
  subject:
    topic: Artificial intelligence
  subject:
    topic: Information theory
  subject:
    topic: Statistical physics
  subject:
    topic: Dynamical systems
  subject:
    topic: Engineering
  subject:
    topic: Electrical engineering
  subject:
    topic: Physics
  subject:
    topic: Statistical Physics, Dynamical Systems and Complexity
  subject:
    topic: Theoretical, Mathematical and Computational Physics
  subject:
    topic: Engineering, general
  subject:
    topic: Information and Communication, Circuits
  subject:
    topic: Communications Engineering, Networks
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: QC174.7-175.36
  classification: 621
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783540288473
  identifier: http://dx.doi.org/10.1007/3-540-28847-3
  record info:
    record creation date: 100301
    record change date: 20151204164021.0
    record identifier: 978-3-540-28847-3