Detection, Tracking and Avoidance of Multiple Dynamic Objects
Journal of Intelligent and Robotic Systems
Spatial understanding and temporalcorrelation for a mobile robot
Spatial Cognition and Computation
Use of neurofuzzy networks to improve wastewater flow-rate forecasting
Environmental Modelling & Software
Navigation behaviors based on fuzzy ArtMap neural networks for intelligent autonomous vehicles
Advances in Artificial Neural Systems
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The procedure for acquiring control rules to improve the performance of control systems has received considerable attention previously. 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 rules, 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 (FNNs). 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