Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

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Table 24.2: Training pattern samples of DOA networks.
INPUT
(Square values)
OUTPUT
11 3 12 5 13 14 6 7 8 4 9 2 10 L T F F F T R
L L R R
1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0
2 0 0 0 1 0 1 1 0 0 1 0 1 0 0 1 0 0 0 0 0
. . . . . . . . . . . . . .. . . . . . . .
55 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
The notation in this table has the same meaning as earlier.
Table 24.3: Training pattern samples of the decision-making network.
No INPUT OUTPUT
L T F F F T R G T F F F T S
L L R R
L L R R T
1 0 1 0 0 0 0 0 0 1 0 0 0 0 0
2 0 0 0 1 0 0 0 0 0 0 1 0 0 0
. . . . . . .
37 1 1 1 1 1 1 1 1 0 0 0 0 0 0
Outputs of Table 24.1 and 24.2 are ANDed and given as INPUT to table 24.3.
Outputs are decision of movement (G= Go; ST= Stop and the others are same as above)
The BP algorithm in our discussion was meant for a closed indoor
environment [21]-[24]. Recently Pomerleau at Carnegei Mellon University
designed an autonomous Land Vehicle called ALVINN that traverses the road
using the Back-propagation algorithm [11], shown in fig.24.19. In their
system, they employed a three layered neural net, with 1024 (32 × 32) neurons
in the input layer, that grabs the (32 × 32) pixel-wise video image and
generates one of the 30 commands at the neurons located at the output layers.
The commands are binary signals, representing the angle changes in the
movement. The figure below describes some of the control signal. A human
trainer generates the control commands for a specific instance of an image.
After training with a few thousand samples of instances, the network becomes
able to generate the control commands autonomously from known input
excitations.