http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Development of neural network calibration algorithms for multi-port pressure probes
application of neural network calibration to this new design
architecture optimization. Techniques for local minima
architecture, and network optimization capabilities.
avoidance and convergence rate improvement, incorporated into
batch mode processing. The study compares the performance of
calibrate multi-port pressure probes for use in wind tunnel
calibrating multi-port probes for flow measurement by
commercial packages' in terms of maximum allowable network
contributes to the current techniques and methods for
demonstrate PROBENET's real world application.
demonstrates the flexibility and utility of the method. To
dimensional velocity data about a delta wing model to
flow analysis. The code offers distinct advantages over
for the calibration of velocity measurement instrumentation
function per layer and multiple activation per layer, and
implementing a robust neural network and training algorithm.
In addition, a unique mini-5-hole probe having a diameter of
in terms of convergence rate and accuracy. The, research
is intended to be a robust self learning code, developed to
is presented. The backpropagation-based program, PROBENET,
layer, as well as heuristics-based procedures for network
of traditional 5- and 7-hole probes is detailed and the
only 0.065" is calibrated and used to measure three
probe is used to demonstrate the accuracy of threecomponent
probe to overcome the flow angularity measurement limitations
PROBENET incorporates multiple activation functions per
prove the capabilities of PROBENET, a 5-hole hemispherical
shows that the latter consistently produces a better solution
size, training convergence rates, flexibility in network
the algorithm, include: momentum, variable learning rate, and
The development of a novel nearly-onmi-directional 18-hole
The development of an enhanced neural network training
two types of network architectures: single activation
velocity prediction over a large range of flow angularity.
Issued also on microfiche from Lange Micrographics.
Includes bibliographical references: pages 61-62.