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: Hierarchical modeling of multi-scale dynamical systems using adaptive radial basis function neural networks
    sub title: application to synthetic jet actuator wing
  name:
    name part: Lee, Hee Eun.
    role:
      role term: creator
  name:
    name part: Junkins, John L.
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2004
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: To obtain a suitable mathematical model of the input-output behavior of highly nonlinear, multi-scale, nonparametric phenomena, we introduce an adaptive radial basis function approximation approach. We use this approach to estimate the discrepancy between traditional model areas and the multiscale physics of systems involving distributed sensing and technology. Radial Basis Function Networks offers the possible approach to nonparametric multi-scale modeling for dynamical systems like the adaptive wing with the Synthetic Jet Actuator (SJA). We use the Regularized Orthogonal Least Square method (Mark, 1996) and the RAN-EKF (Resource Allocating Network-Extended Kalman Filter) as a reference approach. The first part of the algorithm determines the location of centers one by one until the error goal is met and regularization is achieved. The second process includes an algorithm for the adaptation of all the parameters in the Radial Basis Function Network, centers, variances (shapes) and weights. To demonstrate the effectiveness of these algorithms, SJA wind tunnel data are modeled using this approach. Good performance is obtained compared with conventional neural networks like the multi layer neural network and least square algorithm. Following this work, we establish Model Reference Adaptive Control (MRAC) formulations using an off-line Radial Basis Function Networks (RBFN). We introduce the adaptive control law using a RBFN. A theory that combines RBFN and adaptive control is demonstrated through the simple numerical simulation of the SJA wing. It is expected that these studies will provide a basis for achieving an intelligent control structure for future active wing aircraft.
  note: by Hee Eun Lee.
  note: "Major Subject: Aerospace Engineering"
  note: Title from author supplied metadata (record created on Nov. 30, 2005.)
  note: Vita.
  note: Abstract.
  note: Electronic resource.
  note: System requirements: Adobe Acrobat Reader.
  note: M. S. Texas A&M University 2004
  note: Includes bibliographical references.
  note: Text (Thesis).
  subject:
    topic: Major Aerospace Engineering
  subject:
    topic: Adaptive control
  subject:
    topic: Hierarchical modeling
  subject:
    topic: Neural network
  identifier: http://hdl.handle.net/1969.1/230
  record info:
    record content source: TXA
    record creation date: 110525
    record change date: 20151202121900.0
    record identifier: in00002693643