Some Applications of Artificial

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19 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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Some Applications of Artificial
Neural Networks

From Zurada’s book

Neural Networks as Classifiers


This is one of the best known applications
of neural nets


Examples: text recognizers, land feature
recognizer, etc.


However, to begin with we’ll look at a
really simple classifier.

A linear classifier of cube vertices according to this
expression:

What to do when membership
requirements are more complex?


The previous example was easy because
membership requirement was simple


The neural net we used mapped the entire 3D
space into just 2 pts: 1 and

1


The thresholding element used an abrupt sgn
function


This function has no inverse.


Can’t “train” network easily or at all


Bad if membership is not explicitly stated
mathematically

Squashed sgn function


NN’s for real, complex
classification problems use a
“squashed” or smooth sgn
function


Such a function is not really
a sgn function anymore, of
course


But it has an inverse


The invertibility makes the
network more trainable


The training procedures are
pretty independent of the
network architecture or the
problem involved


EEG Spike Recognition


ANNs have been used successfully for preliminary
EEG processing (Eberhart & Dobbins 1990)


Up to 64 electrodes


There are lots of data from all night monitoring


Too much work for doctors, need help


ANN good enough for preprocessing to recognize
certain kinds of signals such as “spikes”

Details


Sampled 4 channels of EEG waves interest


200 or 250 times a second


240ms time window


Yields 48 or 60 data samples from each
channel

Details (2)


Fed data from each channel an ANN with
squashed sgn


41 units arranged in 3 layers does data
processing


Two outputs, one identifying the input as a
spike, the other as a non
-
spike

Design and training teams


Network designed by a team of signal
processing engineers


Training done with 4
-
6 neurologists who
identified spikes or non
-
spikes


Network trained extensively using both
spikes and non
-
spikes


Results


Impressive


6 series of comprehensive tests reported


First, network was tested with spike/non
-
spike waves
used earlier for training


All spikes positively identifed


Only 2 of 260 non
-
spikes misclassified as spikes


Training with totally new data also found all spikes and
misidentified a few non
-
spikes


Since spikes are “bad” this simply result in false alarms,
not undetected trouble


These results were considered better than that which
was required for practical application in hospitals

Function Approximation


Some functions can be slow to compute


But we can often approximate them with a
feed
-
forward neural network (layers with
connections in forward (input
-
to
-
output)
direction only

Function Approximation


21 training
points


31 parameters!


20 weights


11 biases

ALVINN Vehicle Driving
System (Pomerleau, 1989)


2
-
level ANN used for figuring out how
much to turn the steering wheel


45 output neurons with 0/1 outputs


Middle is straight


Leftmost one represents most extreme left
turn


Etc.

ALVINN:
inputs


Consist of
video

and
range

information + 1 extra input


Video info provided by 30x32
retina, depicting road scene


Resulting 960 segments each coded
into input proportional to intensity of
blue


Blue band has highest contrast betw
road and non
-
road)


Range (distance) info provided by
a second retina, 8x32


These units get signal from a laser
rangefinder


1 extra input says whether road is
light or dark at prev time step

ALVINN: training and
performance


Trained with computer
-
generated road images


Involved 1200 different combinations of scenes,
curvatures, lighting conditions and distortion levels


Entire driver implemented on an on
-
board computer
and a modified Chevy van!


Performed comparably to the best traditional vision
-
based navigation systems evaluated under similar
coditions


Training was done in half
-
an
-
hour!


Was training done on board?


For comparison
--

Algorithm
-
based drivers take
months for algorithm development