Neural Network-Based Ship Course Control Simulation using MATLAB

The MATLAB-based neural network simulation program for ship course control represents an intriguing and practical project. This program utilizes neural network algorithms to simulate and optimize ship steering control mechanisms. Through inputting various environmental parameters and vessel characteristics data, the program automatically learns and adjusts neural network weights and biases to achieve more precise and stable course control. The implementation typically involves creating a multi-layer perceptron (MLP) neural network structure with appropriate activation functions (such as tanh or sigmoid) in the hidden layers. The training process employs backpropagation algorithms where the network learns from historical steering data, adjusting connection weights through gradient descent optimization to minimize the error between predicted and actual course directions. Key MATLAB functions used in this implementation may include 'feedforwardnet' for network creation, 'train' for network training with datasets containing wave conditions, wind forces, and vessel dynamics, and 'sim' for simulating the neural network's control responses. The program allows for parameter tuning of learning rates, number of hidden layers, and neuron counts to optimize performance. This simulation program serves not only academic research and educational purposes but also finds applications in actual ship control system development and optimization. By modifying neural network architectures and parameters, researchers can further enhance ship maneuvering performance, ultimately improving maritime safety and operational efficiency. The program demonstrates significant potential for advancing research and applications in the field of intelligent vessel control systems.