I. Hiraga et al., AN ACQUISITION OF OPERATORS RULES FOR COLLISION-AVOIDANCE USING FUZZYNEURAL NETWORKS, IEEE transactions on fuzzy systems, 3(3), 1995, pp. 280-287
The procedure for acquiring control rules to improve the performance o
f control systems has received considerable attention recently. This p
aper deals with a collision avoidance problem in which the controlled
object is a ship with inertia which must avoid collision with a moving
object. It has proven to be difficult to obtain collision avoidance r
oles, i.e., steering rules and speed control rules, which coincide wit
h the operator's knowledge. This paper shows that rules of this type c
an be acquired directly from observational data using fuzzy neural net
works (FNN's). This paper also shows that the FNN can obtain portions
of the fuzzy rules for the inferences of the static and dynamic degree
s of danger and the decision table based on the degrees of danger to a
void the moving obstacle.