Radial Basis Function Neural Network for Tanker Ship Heading Regulation

This approach employs a Radial Basis Function (RBF) neural network for tanker ship heading regulation, enhancing the vessel's ability to maintain course stability during navigation. The RBF neural network is a machine learning algorithm based on neural network architecture that predicts ship heading based on input data and implements corresponding control actions to adjust the vessel's course. The implementation typically involves training the network with historical navigation data, where the hidden layer uses Gaussian activation functions to handle nonlinear relationships. Key functions include calculating Euclidean distances for center selection and optimizing weights through least squares methods. By implementing this method, the ship's heading regulation capability is significantly improved, thereby enhancing both maneuverability and operational safety. The code structure generally involves data preprocessing, network initialization, iterative training cycles, and real-time control output generation.