PSO-Trained BP Neural Network Implementation

This article presents a MATLAB source code example demonstrating BP neural network training using Particle Swarm Optimization (PSO). The implementation includes key components such as neural network architecture initialization, fitness function definition for PSO optimization, and iterative weight adjustment procedures. The code features configurable parameters for hidden layer sizing, PSO swarm size, inertia weights, and convergence criteria. Technical implementations include forward propagation calculations, mean squared error computation for fitness evaluation, and backpropagation-assisted weight updates. The solution incorporates performance metrics like classification accuracy and convergence plots for result validation. Practical applications covered include predictive modeling and pattern classification tasks using the trained network. This executable MATLAB code serves as an educational resource for understanding hybrid PSO-BP optimization techniques and their implementation nuances.