AN ACQUISITION OF OPERATORS RULES FOR COLLISION-AVOIDANCE USING FUZZY NEURAL NETWORKS

The procedure for acquiring control rules to improve the performance of control systems has received considerable attention recently. This paper 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 roles, i.e., steering rules and speed control rules, which coincide with the operator's knowledge. This paper shows that rules of this type can be acquired directly from observational data using fuzzy neural networks (FNN's). This paper also shows that the FNN can obtain portions of the fuzzy rules for the inferences of the static and dynamic degrees of danger and the decision table based on the degrees of danger to avoid the moving obstacle.