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: Adaptive Nonlinear System Identification
    sub title: The Volterra and Wiener Model Approaches
  name:
    name part: Ogunfunmi, Tokunbo.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2007
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 232 p. online resource.
  abstract: Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches introduces engineers and researchers to the field of nonlinear adaptive system identification. The book includes recent research results in the area of adaptive nonlinear system identification and presents simple, concise, easy-to-understand methods for identifying nonlinear systems. These methods use adaptive filter algorithms that are well known for linear systems identification. They are applicable for nonlinear systems that can be efficiently modeled by polynomials. After a brief introduction to nonlinear systems and to adaptive system identification, the author presents the discrete Volterra model approach. This is followed by an explanation of the Wiener model approach. Adaptive algorithms using both models are developed. The performance of the two methods are then compared to determine which model performs better for system identification applications. Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches is useful to graduates students, engineers and researchers in the areas of nonlinear systems, control, biomedical systems and in adaptive signal processing.
  table of contents: to Nonlinear Systems -- Polynomial Models of Nonlinear Systems -- Volterra and Wiener Nonlinear Models -- Nonlinear System Identification Methods -- to Adaptive Signal Processing -- Nonlinear Adaptive System Identification Based on Volterra Models -- Nonlinear Adaptive System Identification Based on Wiener Models (Part 1) -- Nonlinear Adaptive System Identification Based on Wiener Models (Part 2) -- Nonlinear Adaptive System Identification Based on Wiener Models (Part 3) -- Nonlinear Adaptive System Identification Based on Wiener Models (Part 4) -- Conclusions, Recent Results, and New Directions.
  note: by Tokunbo Ogunfunmi.
  subject:
    topic: Mathematics
  subject:
    topic: Image processing
  subject:
    topic: System theory
  subject:
    topic: Control engineering
  subject:
    topic: Robotics
  subject:
    topic: Mechatronics
  subject:
    topic: Electrical engineering
  subject:
    topic: Electronic circuits
  subject:
    topic: Mathematics
  subject:
    topic: Systems Theory, Control
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Control, Robotics, Mechatronics
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Circuits and Systems
  subject:
    topic: Communications Engineering, Networks
  classification: Q295
  classification: QA402.3-402.37
  classification: 519
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Signals And Communication Technology
  identifier: 9780387686301
  identifier: http://dx.doi.org/10.1007/978-0-387-68630-1
  record info:
    record creation date: 100301
    record change date: 20151204142501.0
    record identifier: 978-0-387-68630-1