Information Technology Reference
In-Depth Information
a.
0123456789012345678
901234567890123456
80694
789012
TDUDcTddccabdddadbd
5439
040167431
7 059
93
W = {0.701, 1.117, 0.148, -0.94, -0.044, 1.124, -1.575, 0.877, -1.22, 1.614}
T = {-1.756, -1.776, 0.825, 0.628, -0.263, 0.127, 0.965, -1.651, -0.894, -1.078}
T
0.127
D
U
D
-1.078
0.628
-1.756
c
c
T
d
d
-1.651
c
a
b
b.
0123456789012345678
901234567890123456
80694
789012
TDUDcTddccabdddadbd
9345
040167431
7 059
39
W = {0.701, 1.117, 0.148, -0.94, -0.044, 1.124, -1.575, 0.877, -1.22, 1.614}
T = {-1.756, -1.776, 0.825, 0.628, -0.263, 0.127, 0.965, -1.651, -0.894, -1.078}
T
0.127
D
U
D
0.628
-1.078
-1.756
c
c
T
d
d
-1.651
c
a
b
Figure 10.1. Illustration of Dw-specific and Dt-specific inversion. a) The neural
network before inversion. b) The new neural network created by inversion. Note
that the network architecture is the same before and after inversion and that the
neural networks share the same arrays of weights/thresholds. However, the old and
new neural networks are different because a different constellation of weights and
thresholds is expressed in these individuals.
01234567890123456 7890123456789012 3456
DTQaababaabbaabba 8664826375471750 7682
where “Q” represents a function of four arguments. Suppose that the se-
quence “86648” was chosen as a transposon and that the insertion site was
 
Search WWH ::




Custom Search