7A-3-4 
(11) 
where SM-EuM+EjfvL,) and 
E,M=Ejh.Li)+En(hA)- £,,(£,,i,) and are 
same as the terms in Equation (1). The energy relevant to 
interlayers are 
E\2 =(X \ X 
El 2l =a 2 x 
L )>\ *1 '2 k l 
Ito 
L J'l k 2 <1 k \ 
(12) 
(13) 
B l \kfjc2 is a connectivity variable from neuron (i v k { ) in layer 
L, to neuron (i 2 ,k. 2 ) in layer L,. B 2 is a similar term. They 
change dynamically during iterations. We also 
have £ i2 , m * 2 ^B 2 \k^ because contributions from one layer 
to another layer are non-symmetric. Using energy function 
(11), we can recognize the trucks when a global minimized 
energy value is achieved. 
The connectivity term 
Æ'Vi'A -< 
<fv 
-2xf 
0 
2x V2,^ — I if Line /c, 6 Area k 2 and i 2 = 0 
(14) 
if Line G Area & 2 aw/ / 2 = 1 
otherwise 
contributes when a model region is a truck top and an input line 
belongs to an input region or when the model region is a truck 
shadow and the input line belongs to an input region. Similarly, 
B 2l i 2 kj t k\ is defined as 
B~ i 2 k 2 i t k t — 
2x 
VI,.,*, — — I if Line fc, e Area k^andij =0 
-2x| VI, > ~~ 
if Line e Area k^andi-, = 1 
otherwise 
(15) 
The following describes the process of the two-layer Hopfield 
neural network: 
a) Calculate connectivity parameters C\, kjl of layer 1 and C2 ikj , 
of layer 2. 
b) Set the initial states VI of layer 1 and V2 b of layer 2 
'1*1 '2*2 
using equation (11) respectively. 
c) Obtain B l2 h k t i 2 k 2 using equation (14). 
d) Update the values of III -. and Vf, 
r 'i*i '1*1 
for (Z, = 0; I, < m,; Zj ++) 
for(k ] =0\k x <n x \ k l ++) 
<*:=«!,•*, + ±(*,+2**2 +3*K 3 + K 4 ) 
K, =hxf(u i; iti )=hx(Aj j J j Cl,. iM ,V% - 
B(Zv\ IJ -i)-c'Zvi lil - 
D<L Vi t, - 1 )- £ I V ' 1 /., - 9 iXZ B WA V2 W,> 
j 2**1 12 *2 
*2 =hxf(u i; A + ±tf,) 
^3 =/*X/(Ml! 1 * ) + 7*2> 
^ 4 = /ix/( M i; i , i+ ^ 3 ) 
e) Compute Vl';| = g( M i;;|). 
f) Obtain using equation (15). 
g) Update the values of u2 . and V2 , 
'2*2 '2*2 
for (i 2 = 0; Z 2 < ffl,; i 2 ++) 
for (kj = 0; k 2 < n 2 ; &,++) 
«2;;’ = < * 2 +j;( K] +2*K 2 +3*K 3 + K 4 ) Where 
K, = hxf(u2' i2ki ) = ¿x(AXXC2, 2 * 2 ;,V2', - 
-l)-C^V2 fj/ - 
^1^*2 -5 2 XX fi '2*2',*/ 1 '-,*,) 
j j*k 2 i, *, 
K 2 =hxf{u2\ iki + ±JC,) 
K 3 =hxf(u2'. ki +±K 2 ) 
K 4 =hxf(u2' iik2 + K 2 ) 
h) Update: V2^ = s(«2&) • 
i) If converge, then stop, otherwise go back to step c. 
Process of the two-layer Hopfield neural network