Developed a feed forward neural network to distinguish between true
and false radiation signals and to estimate particle energies in the
presence of a very high level of noise for certain nuclear
reactions. The network consists of an input layer, two hidden
layers, and an output layer. The first layer connections are
developed by using the organization principles found in low-level
mammalian visual system: maximizing information transmission. The
connections of the second and third layers are learned by back error
propagation on the first layer output. The network learns from
training data to do pattern recognition in raw input particle data.
It is shown that the network performs the recognition task with a
high degree of accuracy, higher than trained human eyes.
^{Dong D W and Gyulassy M 1992}
{Neural network approach to process jet fragmentation information}
{Proc International Joint Conference on Neural Networks, Baltimore}
{ Vol~3 pp~191-196}
^{Dong D W and Gyulassy M 1993}
{Neural filters for jet analysis}
{(LBL-31560) Physical Review E}{ Vol~47(4) pp~2913-2922}
^{Dong D W and Chan Y D 1993}
{Three layer network for identifying Cerenkov radiation patterns}
{Proc World Congress on Neural Networks, Portland}{ Vol~1 pp~312-315}
^{Dong D W and Chan Y D 1993}
{Neural network for recognizing Cerenkov radiation patterns}
{LBL-33634}{}
A dynamic neural network is developed to detect soft failures of
sensors and actuators in automobile engines. The network, currently
implemented off-line in software, can process multi-dimensional input
data in real time. The network is trained to predict one of the
variables using others. It learns to use redundant information in
the variables such as higher order statistics and temporal relations.
The difference between the prediction and the measurement is used to
distinguish a normal engine from a faulty one. Using the network, we
are able to detect errors in the manifold air pressure sensor
(Vs) and the exhaust gas recirculation valve (Va) with a high degree
of accuracy.
^{Dong D W, Hopfiled J J, and Unnikrishnan K P 1997}
{Neural networks for engine fault diagnostics}
{Neural Networks for Signal Processing}
{ Vol~VII pp~636-644}
Send comments to Dawei Dong: dawei@dove.ccs.fau.edu