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: Recurrent neural networks for prediction
    sub title: learning algorithms, architectures, and stability
  name:
    name part: Mandic, Danilo P.
    role:
      role term: creator
  name:
    name part: Chambers, Jonathon A.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: enk
    place:
      place term: Chichester
    place:
      place term: New York
    publisher: John Wiley
    date issued: ©2001
    date issued: 2001
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxi, 285 pages) : illustrations
  abstract: New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters. * Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectures together with the concepts of modularity and nesting * Examines stability and relaxation within RNNs * Presents on-line learning algorithms for nonlinear adaptive filters and introduces new paradigms which exploit the concepts of a priori and a posteriori errors, data-reusing adaptation, and normalisation * Studies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iteration * Describes strategies for the exploitation of inherent relationships between parameters in RNNs * Discusses practical issues such as predictability and nonlinearity detecting and includes several practical applications in areas such as air pollutant modelling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing Recurrent Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and, consequently, will have instant appeal. It provides an extensive background for researchers, academics and postgraduates enabling them to apply such networks in new applications.
  note: Danilo P. Mandic, Jonathon A. Chambers.
  note: Electronic reproduction. [Place of publication not identified] : HathiTrust Digital Library, 2010. MiAaHDL
  note: Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL
  note: digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL
  note: Includes bibliographical references (pages 267-280) and index.
  note: English.
  subject:
    topic: Machine learning
  subject:
    topic: Neural networks (Computer science)
  classification: Q325.5 .M36 2001eb
  classification: 006.3/2
  related item:
    title info:
      title: Recurrent neural networks for prediction
    name:
      name part: Mandic, Danilo P.
    origin info:
      publisher: Chichester ; New York : John Wiley, ©2001
    identifier: (DLC) 2001033418
  related item:
    title info:
      title: Adaptive and learning systems for signal processing, communications, and control
  identifier: 0470852399
  identifier: 9780470852392
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  identifier: 1280554533
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  identifier: 9780471495178
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NhCcYBP
    record creation date: 030226
    record change date: 20260214203404.2
    record identifier: in00004678143
    language of cataloging:
      language term: eng