MATLAB Program for Training BP and RBF Neural Networks Using Improved Particle Swarm Optimization

In this technical documentation, we provide an extended explanation of how to use the improved Particle Swarm Optimization (PSO) algorithm to train both Backpropagation (BP) and Radial Basis Function (RBF) neural networks through MATLAB implementation. The enhanced PSO algorithm represents an advanced optimization technique that simulates the collective foraging behavior of bird flocks to efficiently locate optimal solutions in complex search spaces. This algorithm significantly improves neural network training by optimizing weight matrices and bias parameters, thereby enhancing overall network performance and prediction accuracy. The MATLAB program implements key components including: particle initialization with velocity constraints, fitness evaluation using mean squared error functions, dynamic inertia weight adjustment, and cognitive/social acceleration coefficients for balanced exploration-exploitation tradeoffs. For BP neural networks, the program incorporates gradient descent optimization with PSO-enhanced weight updates through matrix operations and activation function computations. The RBF network implementation focuses on optimizing center positions, widths of radial basis functions, and output layer weights using Gaussian kernel functions and Euclidean distance calculations. The program structure includes main training loops that handle particle position updates, fitness comparisons, and global best tracking. Key MATLAB functions employed are 'pso()' for optimization core, 'trainBP()' for backpropagation processing, and 'trainRBF()' for radial basis function network training. Additional utility functions manage data normalization, performance metrics calculation, and convergence visualization. By implementing this program, users achieve superior convergence characteristics during neural network training, resulting in more accurate predictive models and enhanced computational efficiency. The code includes configuration parameters for adjusting swarm size, iteration limits, and network architecture specifications to accommodate various dataset characteristics and application requirements.