Training BP Neural Network Using PSO Particle Swarm Optimization Algorithm

This program utilizes Particle Swarm Optimization (PSO) algorithm to train Backpropagation (BP) neural networks in MATLAB environment. The code implementation employs PSO's global optimization capabilities to optimize BP neural network's initial weights and thresholds, effectively preventing local minima trapping and accelerating convergence. With clear modular structure, the code includes key functions for particle initialization, fitness evaluation (typically using mean squared error as objective function), velocity/position updates following PSO dynamics, and neural network weight adjustments. The implementation allows flexible parameter customization including swarm size, inertia weight, and learning factors. Users can easily modify network architecture (hidden layers/neurons) and PSO parameters according to specific requirements. This straightforward yet effective approach significantly improves BP network training performance while maintaining MATLAB's user-friendly operational convenience, making it suitable for various pattern recognition and prediction tasks.