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: Development of neural network calibration algorithms for multi-port pressure probes
  name:
    name part: Kinser Robert Eric.
    role:
      role term: creator
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: xx
    date issued: 1996
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    form: remote
    extent: x, 108 leaves : illustrations ; 28 cm.
  abstract: algorithm and program
  abstract: application of neural network calibration to this new design
  abstract: architecture optimization. Techniques for local minima
  abstract: architecture, and network optimization capabilities.
  abstract: avoidance and convergence rate improvement, incorporated into
  abstract: batch mode processing. The study compares the performance of
  abstract: calibrate multi-port pressure probes for use in wind tunnel
  abstract: calibrating multi-port probes for flow measurement by
  abstract: commercial packages' in terms of maximum allowable network
  abstract: contributes to the current techniques and methods for
  abstract: demonstrate PROBENET's real world application.
  abstract: demonstrates the flexibility and utility of the method. To
  abstract: dimensional velocity data about a delta wing model to
  abstract: flow analysis. The code offers distinct advantages over
  abstract: for the calibration of velocity measurement instrumentation
  abstract: function per layer and multiple activation per layer, and
  abstract: implementing a robust neural network and training algorithm.
  abstract: In addition, a unique mini-5-hole probe having a diameter of
  abstract: in terms of convergence rate and accuracy. The, research
  abstract: is intended to be a robust self learning code, developed to
  abstract: is presented. The backpropagation-based program, PROBENET,
  abstract: layer, as well as heuristics-based procedures for network
  abstract: of traditional 5- and 7-hole probes is detailed and the
  abstract: only 0.065" is calibrated and used to measure three
  abstract: probe is used to demonstrate the accuracy of threecomponent
  abstract: probe to overcome the flow angularity measurement limitations
  abstract: PROBENET incorporates multiple activation functions per
  abstract: prove the capabilities of PROBENET, a 5-hole hemispherical
  abstract: shows that the latter consistently produces a better solution
  abstract: size, training convergence rates, flexibility in network
  abstract: the algorithm, include: momentum, variable learning rate, and
  abstract: The development of a novel nearly-onmi-directional 18-hole
  abstract: The development of an enhanced neural network training
  abstract: two types of network architectures: single activation
  abstract: velocity prediction over a large range of flow angularity.
  note: by Robert Eric Kinser.
  note: "Major subject: Aerospace Engineering".
  note: Vita.
  note: Also available online.
  note: Issued also on microfiche from Lange Micrographics.
  note: M.S. Texas A&M University 1996.
  note: Includes bibliographical references: pages 61-62.
  subject:
    topic: Major aerospace engineering
  identifier: https://hdl.handle.net/1969.1/ETD-TAMU-1996-THESIS-K57
  related item:
    location:
      url: https://hdl.handle.net/1969.1/ETD-TAMU-1996-THESIS-K57
  record info:
    record content source: TXA
    record creation date: 970701
    record change date: 20220103134537.0
    record identifier: in00001361152