Abstract
A fuzzy-neural network for ship collision avoidance where ships are in sight of one another is proposed in this article. There are three subsets: the subset of classifying ship encounter situations and collision avoidance actions, the subset of calculating the membership functions of speed ratio, and the subset of inferring alteration magnitude and action time. The weight values of the former two subsets are obtained by self-learning from a number of samples, while those of the last subset are obtained from experience. The test results show that by the use of this network, some valuable decisions can be made.
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© 2006 Springer-Verlag Berlin Heidelberg
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Liu, YH., Du, XM., Yang, SH. (2006). The Design of a Fuzzy-Neural Network for Ship Collision Avoidance. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_84
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DOI: https://doi.org/10.1007/11739685_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
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