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- W1992955930 abstract "In this paper, an online self-constructing fuzzy neural identification for MMG ship motion model is clearly identified by using the promising Generalized Ellipsoidal Function Based Fuzzy Neural Network (GEBF-FNN) method. Nonlinear differential equations of MMG-type ship motion dynamics are used to establish the reference model implicating essential nonlinearities for GEBF-FNN based ship motion model (GEBF-FNN-SMM) identification. The GEBF-FNN-SMM starts without fuzzy rules and online recruits efficient fuzzy rules via rule node generation criteria and parameter estimation. The resultant GEBF-FNN-SMM reasonably captures essential dynamics since the checking process validates the prediction performance with high accuracy. Finally, in order to demonstrate that the GEBF-FNN-SMM scheme is effective, simulation studies are conducted on zig-zag maneuvers. Moreover, comprehensive comparisons are carefully presented. Simulation results indicate that the GEBF-FNN-SMM achieves promising performance in terms of approximation and prediction." @default.
- W1992955930 created "2016-06-24" @default.
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- W1992955930 date "2012-07-01" @default.
- W1992955930 modified "2023-10-16" @default.
- W1992955930 title "Online self-constructing fuzzy neural identification for ship motion dynamics based on MMG model" @default.
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- W1992955930 doi "https://doi.org/10.1109/wcica.2012.6357919" @default.
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