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

known. The aim is to estimate the steady-state belief of all propositions in the
network. Since stability of the reasoning model is guaranteed, the belief
revision process is continued until steady- state is reached. In fact steady-state
occurs in the reasoning model of case-III after 5 belief revision cycles. Once
the steady-state condition is reached, the network may be used for generating
new inferences.
Initial Weights wij
Table 20.1: Parameters of case history I.
Initial Fuzzy Beliefs ni
w71=0.8, w72=0.7, w93=0.6,
w14=0.9, w13,5=0.8, w13,6=0.5
n1=0.2, n2=0.8, n3=0.75,
n4=0.9,
n5=0.6, n6=0.75, n7=0.35,
n8=0.85, n9=0.45, n10=0.85,
n11=0.7, n12=0.65, n13=0.
Steady-state weights after 4 iterations w71=0.35, w72=0.60, w93=0.35,
thj =0 for all transitions trj
w14=0.35, w13,5=0.35,
w13,6=0.50
The FPN, given in fig. 20.4, has been formed using the above rule-base and
database from a typical case history. The fuzzy beliefs of the places in fig.
20.4 are found from a proven historical database. The initial weights in the
network are assigned arbitrarily and the model for encoding of weights is used
for computing the steady-state value of weights (vide Table 20.1).