A fuzzy-neural inference network for ship collision avoidance

Abstract

The basic structure of a fuzzy-neural inference network model for ship collision avoidance in sight one another is presented in this article. The model has three subnets. There are the subset of classifying ship encounter situations and collision avoidance actions, the subset of calculating membership function of speed ratio, and the subset of inferring alteration magnitude and action time. The weight values of former two subsets are obtained by self-learning from a number of samples, while those of last one subset are obtained form experience. All of these weight values can be adjusted respectively and conveniently according to practical needs. The test results show that by the inference of the model, some valuable decisions can be made from initial input data.