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Synapse
April 30, 2026Open Access

BiGRU-MHA-KAN based flight training trajectory prediction

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Overview

Randomized trial improves trajectory prediction accuracy in flight training, indicating enhanced reliability of models.

Key Points

  • The study aims to enhance trajectory prediction accuracy in flight training using a hybrid neural network model.
  • Developed the BiGRU-MHA-KAN model integrating bi-directional GRU, multi-head attention, and Kolmogorov-Arnold networks.
  • Conducted simulated experiments to assess the impact of parameter settings and historical data volumes.
  • Analyzed model performance through improvements in prediction accuracy and error reduction metrics.
  • Achieved an improvement in prediction accuracy of 4.81%-5.83% compared to other models.
  • Significantly reduced mean squared error and root mean squared error metrics.
  • Demonstrated stronger temporal modeling capability and stability during flight training scenarios.

Cite This Study

A 2026 study studied this question.

synapsesocial.com/papers/69f2f1dc1e5f7920c638779fhttps://doi.org/10.1051/jnwpu/20264410185/pdf
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