PSO-Optimized BP Neural Network Implementation

This implementation demonstrates how Particle Swarm Optimization (PSO) leverages rapid convergence properties while neural networks excel in global search capabilities. The integration of these two algorithms creates a synergistic effect that enhances overall optimization performance. The program architecture includes key components such as particle position updates using velocity vectors, fitness evaluation through mean squared error calculations, and neural network weight optimization via gradient descent. After thorough debugging, we have successfully combined these algorithms with features including dynamic inertia weight adjustment and momentum-based gradient optimization. The method's superiority has been validated in practical applications, establishing it as a powerful approach for future optimization challenges. The code implementation specifically handles swarm initialization, forward propagation with sigmoid activation functions, and backpropagation with learning rate adaptation.