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This study introduces an improved sparrow search algorithm (SSA) with Sine chaos mapping to enhance BP neural network accuracy for inland vessel trajectory prediction, overcoming local optima issues.

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Area of Science:

  • Artificial Intelligence
  • Marine Navigation Systems
  • Computational Optimization

Background:

  • Traditional Backpropagation (BP) neural networks struggle with accuracy and local optima in complex trajectory prediction tasks.
  • Inland river vessel navigation requires precise trajectory prediction for safety and efficiency.

Purpose of the Study:

  • To develop an optimized BP neural network model for accurate inland river vessel trajectory prediction.
  • To address the limitations of standard BP networks, specifically their tendency to get trapped in local optima and exhibit poor accuracy.

Main Methods:

  • A standard BP neural network model was established using Automatic Identification System (AIS) data from ships on the Yangtze River.
  • A Sine-BP model was developed, incorporating Sine chaos mapping for neural network weight and threshold initialization.
  • A Sine-SSA-BP model was constructed, utilizing an improved Sparrow Search Algorithm (SSA) to optimize the BP network's weights and thresholds.

Main Results:

  • The Sine-SSA-BP model demonstrated improved initialization of the population distribution, mitigating premature convergence issues common in population intelligence algorithms.
  • The optimized model showed enhanced performance compared to standard initialization methods.

Conclusions:

  • The Sine-SSA-BP neural network significantly outperforms conventional Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) models in trajectory prediction accuracy and stability.
  • The model shows particular strength in predicting vessel turns, aligning well with actual ship navigation trajectories.